[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-743":3,"related-743":62},{"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":8,"secondary_topics":32,"classification":33,"search_phrases":56,"slug":59,"view_count":60,"doi":8,"paper":8,"created_at":61},743,"中国农业新质生产力与绿色发展绩效的耦合协调度研究","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751","基于30省2013-2023面板,采用全球非径向VRS-SBM-GML模型测度农业绿色全要素生产率(AGTFP);耦合协调度从2013年0.3631升至2023年0.6072,新型劳动对象\u002F劳动资料维度率先进入初级协调,新型劳动力维度仍处勉强协调(0.5992)。",null,"## 1. Introduction\n\nDeveloping agricultural new quality productive forces is an important pathway for shifting the drivers of agricultural development from conventional factor inputs toward scientific and technological innovation and optimized factor allocation, thereby improving the quality of agricultural development. Compared with agriculturally advanced countries, China still has considerable scope to improve agricultural labor productivity, the allocation of scientific and technological resources, and the commercialization of agricultural research outputs. Jiang [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B1-agriculture-16-01751)] estimated that, in 2020, agricultural labor productivity in the United States, Canada, and Israel was approximately 17.3, 19.8, and 25 times that of China, respectively. Meanwhile, increasingly stringent constraints on cultivated land, water resources, and the ecological environment have reduced the marginal returns of the conventional agricultural growth model, which relies primarily on expanding land, labor, machinery, and chemical inputs. A shift in development momentum through technological innovation, the reorganization of production factors, and changes in production modes is therefore urgently needed. The application of emerging technologies, including biological breeding, intelligent agricultural machinery, and artificial intelligence, has created new conditions for restructuring agricultural production functions and upgrading the form of agricultural productive forces, while drawing greater attention to the coordination between the upgrading of agricultural productive forces and resource–environmental performance.\n\nAgricultural new quality productive forces refer to an advanced form of agricultural productive forces led by agricultural scientific and technological innovation, grounded in improvements in worker quality, the modernization of the means of labor, and the optimization of the objects of labor, and formed through the reorganization of production factors and changes in production modes. They are characterized by high technology intensity, high efficiency, high quality, and greenness [[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B2-agriculture-16-01751)]. Their “new quality” lies primarily in changes in factor quality, technological forms, and allocation patterns, rather than in a rejection of the basic components of conventional agricultural productive forces. Xi [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B3-agriculture-16-01751)] noted that green development is the defining feature of high-quality development and that new quality productive forces are inherently green productive forces, indicating an intrinsic consistency in the value orientation of the upgrading of productive forces and green development. Agricultural green development, in turn, emphasizes improving resource-use efficiency and reducing environmental burdens while maintaining agricultural output. The two concepts reflect the transformation of agricultural production modes from the perspectives of factor upgrading and resource–environmental performance improvement, respectively; however, their development levels and rates of evolution are not necessarily synchronized.\n\nIn recent years, China has made overall progress in agricultural green development. The composite agricultural green development index reported in the China Agricultural Green Development Report 2024 [[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B4-agriculture-16-01751)] indicates that the national level of agricultural green development in 2023 was higher than both the previous year and 2015. Nevertheless, China’s agricultural green transition still faces practical constraints, including relatively low input-use efficiency. Data released by the Department of Science, Technology and Education of the Ministry of Agriculture and Rural Affairs [[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B5-agriculture-16-01751)] show that, in 2020, the fertilizer- and pesticide-use efficiencies of China’s three major grain crops were 40.2% and 40.6%, respectively. These indicators represent the shares of applied fertilizer nutrients absorbed by crops in the current season and of pesticides effectively used against target pests. Thus, although agricultural output has continued to grow, substantial room remains to improve resource-use efficiency and control environmental burdens. Whether the upgrading of agricultural productive forces can remain synchronized with improvements in resource–environmental performance has therefore become an important issue in transforming the agricultural development mode.\n\nExisting studies have examined the relationship between ANQPF and agricultural green development, but the coordination differences among the constituent dimensions of productive forces and the dynamic evolution of coordination states have not yet been systematically investigated. Because agricultural green development encompasses economic growth, resource use, ecological protection, social welfare, and governance institutions, this study does not construct a composite measure of agricultural green development in the broad sense. Instead, it focuses on the resource–environmental performance dimension and uses agricultural green total factor productivity (AGTFP) under resource and environmental constraints to represent AGDP. Against this background, the objective of this study is to characterize the temporal evolution, dimension-specific coordination, and state-transition patterns between ANQPF and AGDP across Chinese provinces. Specifically, using data for 30 Chinese provincial-level regions from 2013 to 2023, this study addresses three questions: (1) What annual evolutionary patterns characterize ANQPF and AGDP? (2) How does the coordination between AGDP and ANQPF as a whole, as well as its three constituent dimensions—new-quality means of labor, new-quality labor force, and new-quality objects of labor—differ? (3) How persistent are different coordination states, and in which directions do they transition?\n\n## 2. Literature Review\n\nResearch on agricultural green development has focused mainly on its conceptual connotation, influencing factors, and measurement methods. Existing studies generally define agricultural green development as the coordinated pursuit of agricultural economic growth, resource conservation, and ecological and environmental protection while ensuring an effective supply of agricultural products [[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B6-agriculture-16-01751)]. Related research has further examined the determinants of agricultural green development from the perspectives of scientific and technological innovation, digital transformation, industrial restructuring, land-factor allocation, and agricultural socialized services, suggesting that regional resource endowments, technological foundations, and institutional conditions shape the effectiveness of the green transition [[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B7-agriculture-16-01751)]. In terms of measurement, two principal approaches are used: composite indicator evaluation and green total factor productivity. The former captures the multidimensional objectives of agricultural green development, whereas the latter incorporates resource inputs, desirable outputs, and undesirable outputs into a unified production frontier and focuses on input–output performance under resource and environmental constraints [[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B8-agriculture-16-01751),[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B9-agriculture-16-01751),[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B10-agriculture-16-01751)]. Because this study examines the coordination between the upgrading of productive forces and resource–environmental performance, AGTFP is used to represent AGDP, but it is not treated as equivalent to agricultural green development in the broad sense.\n\nAgricultural new quality productive forces are rooted in the Marxist theory of productive forces and represent a contextual development of this theory amid a new round of technological revolution and agricultural modernization. Workers, means of labor, and objects of labor remain the basic components of agricultural productive forces. ANQPF do not alter this fundamental logic; rather, they reflect the upgrading of factor quality, technological forms, and factor combinations under the leadership of scientific and technological innovation [[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B11-agriculture-16-01751)]. Compared with conventional agricultural productive forces, which depend largely on quantitative expansion of land, labor, machinery, and chemical inputs, ANQPF place greater emphasis on technological innovation, the application of digital technologies, and optimized factor combinations [[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B12-agriculture-16-01751)]. Existing studies have mainly addressed conceptual definition, functional effects, and level measurement. They generally characterize ANQPF as an advanced form of productive forces led by scientific and technological innovation and formed through qualitative upgrading and optimized combinations of production factors, and they construct evaluation systems either around the three elements of productive forces or around functional dimensions such as technology, digitalization, and greenness [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B13-agriculture-16-01751),[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B14-agriculture-16-01751),[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B15-agriculture-16-01751),[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B16-agriculture-16-01751)]. Common measurement methods include the entropy-weight method, entropy-weight TOPSIS, and Markov chains. These studies generally find that ANQPFs in China have increased overall, although substantial regional disparities remain [[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B17-agriculture-16-01751)].\n\nResearch on the relationship between ANQPF and green performance has followed two main analytical approaches: unidirectional effects and system coordination. Econometric studies primarily examine the effects of ANQPF on agricultural green production efficiency, carbon-emission efficiency, and sustainable-development performance, treating technological innovation, digital transformation, industrial upgrading, and factor optimization as potential channels [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B16-agriculture-16-01751),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B18-agriculture-16-01751),[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B19-agriculture-16-01751),[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B20-agriculture-16-01751)]. Other studies adopt a system-coordination perspective to investigate the relationships between new quality productive forces and agricultural green development, between digitalization and greening, and between ANQPF and green grain-production capacity. They generally find that coordination has improved, although regional disparities remain pronounced [[21](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B21-agriculture-16-01751),[22](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B22-agriculture-16-01751),[23](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B23-agriculture-16-01751)]. Consistent with these findings, research on sustainable intensification, precision agriculture, digital agriculture, and agricultural innovation systems indicates that technological upgrading can improve resource use and environmental performance, but the extent to which these benefits materialize depends on infrastructure, human capital, service systems, and the institutional environment [[24](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B24-agriculture-16-01751),[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B25-agriculture-16-01751),[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B26-agriculture-16-01751),[27](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B27-agriculture-16-01751),[28](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B28-agriculture-16-01751),[29](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B29-agriculture-16-01751)]. The upgrading of agricultural productive forces and improvements in green performance are therefore not necessarily synchronized.\n\nOverall, the existing literature has three limitations. First, most studies focus on the unidirectional effect of ANQPF on green performance and pay insufficient attention to the synchronization and structural alignment of the two systems. Second, some studies include greening indicators in both ANQPF and agricultural green development assessments, leaving the measurement boundaries between the two systems insufficiently defined. Third, existing coordination studies primarily examine overall levels and spatial disparities, while giving limited attention to coordination differences among the three dimensions—new-quality means of labor, new-quality labor force, and new-quality objects of labor—or to the persistence, upgrading, and decline of coordination states. To address these gaps, this study uses AGTFP to represent AGDP, examines the coupling coordination between AGDP and both ANQPF as a whole and its three constituent dimensions, and applies a Markov chain to analyze the dynamic evolution of coordination states.\n\nThe marginal contributions of this study are threefold. First, AGTFP is used to represent AGDP, distinguishing this approach from composite assessments that incorporate economic, social, and ecological dimensions and thereby clarifying the measurement boundaries between ANQPF and AGDP. Second, the analysis extends from overall coordination to the three dimensions of new-quality means of labor, new-quality labor force, and new-quality objects of labor, allowing differences in the alignment between distinct elements of productive forces and AGDP to be compared. Third, in addition to examining coordination levels and spatial distributions, a Markov chain is introduced to identify the persistence, upgrading, and decline of coordination states. The coupling coordination degree reflects the synchronization, alignment, and coordination of the two systems; it is not used to identify the direction of influence or a causal relationship between them.\n\n## 3. Theoretical Analysis\n\nThis study draws on the theory of productive forces, system-coupling theory, and evolutionary and path-dependence theory to examine the relationship between ANQPF and AGDP at three levels: overall coordination, dimension-specific coordination, and state transitions. The purpose is to evaluate the synchronization and structural alignment of the two systems rather than to identify causal relationships among variables. Accordingly, the following theoretical propositions concern coordination states and their evolution and do not presuppose a causal direction.\n\n### 3.1. Coupling Coordination Between Agricultural New Quality Productive Forces and Agricultural Green Development Performance\n\nSystem-coupling theory holds that the coordination state between systems depends on the relative alignment of their development levels, evolutionary rates, and internal structures. ANQPF and AGDP are jointly embedded in agricultural production and operation. New-quality means of labor, new-quality labor force, and new-quality objects of labor reflect qualitative upgrading in production conditions, production actors, and production objects, whereas AGDP captures changes in aggregate output under resource and environmental constraints. Changes in technological equipment, digital infrastructure, and human capital occur within the same production system as changes in agriculture-related environmental outputs. The development states of the two systems may therefore correspond, but they are not necessarily synchronized. Differences in technological suitability, resource endowments, and development stages may generate divergence in their levels and rates of change: some regions may accumulate new-quality factors rapidly while AGDP lags, whereas others may exhibit the opposite pattern [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B13-agriculture-16-01751),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B18-agriculture-16-01751),[30](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B30-agriculture-16-01751)]. The coupling coordination degree measured in this study is intended to reflect the synchronization and alignment of the two systems rather than to identify causal effects. Accordingly, the following hypothesis is proposed:\n\n**Hypothesis 1****(H1).**\n\nANQPF and AGDP exhibit a general trend of joint improvement, but their growth trajectories are not fully synchronized; consequently, the overall level of coupling coordination increases over time.\n\n### 3.2. Differentiated Coordination Between the Dimensions of Agricultural New Quality Productive Forces and Agricultural Green Development Performance\n\nThe theory of productive forces regards workers, means of labor, and objects of labor as the basic components of productive forces. ANQPF thus represent the qualitative upgrading and recombination of these three components driven by scientific and technological innovation [[31](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B31-agriculture-16-01751)]. Because the foundations, evolutionary cycles, and regional dependence of the three dimensions differ, each may form a distinct coordination relationship with AGDP. New-quality means of labor reflect upgrades in technological equipment and digital infrastructure. Although technological renewal is relatively rapid, its diffusion is uneven, and its rate of change may be faster or slower than that of AGDP, producing different alignment states [[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B14-agriculture-16-01751),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B18-agriculture-16-01751),[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B25-agriculture-16-01751)]. The new-quality labor force reflects the accumulation of human capital and skills. Its formation and allocation require longer periods, while the efficiency with which it is transformed into productive outcomes depends on training and extension systems [[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B26-agriculture-16-01751),[28](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B28-agriculture-16-01751)]; its coordination with AGDP may therefore differ from that of the means-of-labor dimension. New-quality objects of labor reflect changes in how production objects are developed and utilized. They are strongly dependent on resources and location [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B16-agriculture-16-01751),[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B20-agriculture-16-01751)], and their optimization and value realization exhibit marked path dependence, generating coordination characteristics distinct from those of the other two dimensions. Accordingly, the following hypothesis is proposed:\n\n**Hypothesis 2****(H2).**\n\nThe coordination levels and evolutionary pathways between AGDP and the three dimensions of ANQPF—new-quality means of labor, new-quality labor force, and new-quality objects of labor—differ.\n\n### 3.3. Dynamic Evolution of Coupling Coordination States\n\nEvolutionary and path-dependence theory indicates that technologies, knowledge, and institutional rules are cumulative. System transformation evolves incrementally along established trajectories but may stagnate or regress in response to external shocks [[32](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B32-agriculture-16-01751)]. Coordination states are built on existing technological and institutional arrangements. Technology diffusion and human-capital development are generally long-term processes, making cross-stage transitions difficult to achieve in the short run. Interdependence between technologies and institutions creates path inertia, and transformation usually occurs through incremental reconfiguration [[27](https","MDPI Agriculture 16(16):1751 2026-08-15","2026-08-15T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},20,22,18,12,4,1,"研究中国农业新质生产力与绿色发展绩效的耦合协调度，具有全国性政策意义，数据详实，但时效性稍差。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","新质生产力","AGTFP","耦合协调度","30省",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"unclassified","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":54,"reason":55},[39,44,49],{"code":40,"score":22,"supported":14,"title":41,"abstract":42,"tags":43},"I4",[],[],[30],{"code":45,"score":22,"supported":14,"title":46,"abstract":47,"tags":48},"I6",[],[],[27],{"code":50,"score":22,"supported":14,"title":51,"abstract":52,"tags":53},"J2",[],[],[28],true,"缺少足够的主题证据",[57,58],"新质生产力 耦合协调度 数字乡村 30省","新质生产力 耦合协调度","新质生产力耦合协调度数字乡村30省-743",2,"2026-08-19T23:57:32.301681Z",{"total":63,"page":22,"page_size":63,"items":64},6,[65,129,174,222,264,308],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":8,"content":8,"source_name":70,"source_url":8,"published_at":71,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":72,"score_detail":73,"sources":77,"tags":79,"primary_topic":83,"secondary_topics":88,"classification":89,"search_phrases":113,"slug":116,"view_count":21,"doi":117,"paper":118,"created_at":128},4153,"中国农业农村数字化绿色化协同发展的时空格局及其增收效应","https:\u002F\u002Fcjournal.hep.com.cn\u002F1007-4333\u002FCN\u002F10.11841\u002Fj.issn.1007-4333.2026.10.06","蔡雪玲（中国农业大学学报2026年第10期，兰州财经大学会计学院）。文章立足于农业农村「生产-管理-生态-创新-生活」数绿协同的内在逻辑构建测度指标体系，采用熵值法、改进的耦合协调度模型、Kernel密度估计与空间Markov链转移矩阵测算农业农村数绿协同发展水平并刻画其时空演进特征，构建固定效应模型与空间计量模型检验农业农村数绿协同发展对农村居民收入的影响关系与空间溢出效应。结果表明：全国及各省份农业农村数绿协同发展水平缓慢上升，尚处于基本协同阶段。","中国农业大学学报2026年第10期","2026-10-01T00:00:00Z",80,{"impact":19,"substance":18,"depth":19,"authority":74,"freshness":75,"relevant":22,"comment":76},14,8,"核心期刊论文，构建数绿协同测度体系并实证检验其增收效应与空间溢出，方法规范、结论有政策参考价值，但属学术研究而非产业动态，适合主题聚合而非每日精选头条。",[78],{"name":70,"url":68},[27,80,81,30,82],"农业绿色发展","农村居民收入","空间溢出效应",{"code":40,"slug":84,"name":85,"path":86,"parent_code":87},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":90,"locked":14,"version":35,"method":36,"evidence":91},"classified",{"candidates":92,"agricultural_evidence":54},[93,99,104,109],{"code":40,"score":94,"supported":54,"title":95,"abstract":96,"tags":98},5,[],[97],"空间计量",[82,30],{"code":100,"score":22,"supported":14,"title":101,"abstract":102,"tags":103},"H4",[],[],[80],{"code":105,"score":22,"supported":14,"title":106,"abstract":107,"tags":108},"H8",[],[],[80],{"code":45,"score":22,"supported":14,"title":110,"abstract":111,"tags":112},[],[],[27],[114,115],"农业农村 数字化绿色化 协同发展","农村居民收入 空间溢出效应","农业农村数字化绿色化协同发展-4153","10.11841\u002Fj.issn.1007-4333.2026.10.06",{"doi":117,"openalex_id":8,"authors":119,"venue":8,"cited_by_count":120,"oa_url":8,"card":121,"direction":125,"ingested_from":127},[],0,{"tldr":122,"method":123,"finding":124,"direction":125,"opportunity":126},"测度中国农业农村数字化绿色化协同水平并检验其时空格局与增收效应。","熵值法、耦合协调度模型、Kernel密度、空间Markov链与空间计量模型。","全国及各省数绿协同水平缓慢上升，仍处基本协同阶段，且对农村居民收入有影响与空间溢出。","数字乡村与农业信息化","可深入探究数绿协同增收效应的区域异质性与微观农户传导机制，识别关键短板维度。","agent","2026-10-04T00:09:09.731896Z",{"id":130,"title":131,"url":132,"summary":133,"summary_zh":8,"content":8,"source_name":134,"source_url":8,"published_at":135,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":72,"score_detail":136,"sources":139,"tags":141,"primary_topic":8,"secondary_topics":145,"classification":146,"search_phrases":162,"slug":165,"view_count":60,"doi":8,"paper":166,"created_at":173},3448,"《新质生产力赋能农业数智化转型论析》","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fhznydxxbshkxb\u002Fmulu\u002F559178.html","论文阐述新质生产力为农业数智化出场提供马克思主义生产力理论基础；指明新一轮科技革命和产业变革的历史交汇；阐明农业数智化的跃迁升级源于新质生产力的持续赋能。指出新质生产力赋能农业数智化转型面临'人-物-链'系统性、闭环型困境：农业数智化人才匮乏、基础设施滞后、产业链供应链集成化水平较低、产业政策支持力度不足。建议以推动农业颠覆性科技创新运用为核心，以构建农业数智化生态链和加速农业全要素提质增效为两翼。","《华中农业大学学报（社会科学版）》","2026-09-20T00:00:00Z",{"impact":18,"substance":137,"depth":19,"authority":74,"freshness":94,"relevant":22,"comment":138},21,"核心期刊论文，从马克思主义生产力理论切入系统剖析农业数智化转型的“人-物-链”困境与对策，理论深度与政策参考价值兼具，但属学术论析而非新政策或数据发布，时效性一般。",[140],{"name":134,"url":132},[27,142,28,143,144],"智慧农业","农业科技创新","农业数智化",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":147},{"candidates":148,"agricultural_evidence":54,"reason":55},[149,154,158],{"code":150,"score":60,"supported":14,"title":151,"abstract":152,"tags":153},"I1",[],[],[144,142],{"code":50,"score":60,"supported":14,"title":155,"abstract":156,"tags":157},[],[],[143,28],{"code":45,"score":22,"supported":14,"title":159,"abstract":160,"tags":161},[],[],[27],[163,164],"华中农业大学学报 农业数智化","新质生产力 农业转型","华中农业大学学报农业数智化-3448",{"doi":8,"openalex_id":8,"authors":167,"venue":8,"cited_by_count":120,"oa_url":8,"card":168,"direction":125,"ingested_from":127},[],{"tldr":169,"method":170,"finding":171,"direction":125,"opportunity":172},"从马克思主义生产力理论出发，分析新质生产力赋能农业数智化转型的困境与路径。","理论分析与政策论述，围绕'人-物-链'框架展开。","农业数智化面临人才匮乏、设施滞后、产业链集成低、政策支持不足等系统性困境。","可针对'人-物-链'困境开展实证测度与区域差异研究，检验政策干预效果。","2026-09-25T00:09:34.724043Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":8,"content":8,"source_name":179,"source_url":8,"published_at":180,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":181,"score_detail":182,"sources":187,"tags":189,"primary_topic":8,"secondary_topics":192,"classification":193,"search_phrases":209,"slug":212,"view_count":213,"doi":8,"paper":214,"created_at":221},2613,"新质生产力赋能农村基层数字治理的困境与路径","https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1074544479_121123708","《当代农村财经》2026年第9期刊发。探讨新质生产力赋能农村基层数字治理的困境与路径。","当代农村财经 2026年第9期","2026-09-10T01:00:00Z",70,{"impact":183,"substance":19,"depth":184,"authority":185,"freshness":63,"relevant":22,"comment":186},16,17,13,"核心期刊论文，聚焦新质生产力赋能农村基层数字治理的困境与路径，属数字乡村治理议题，有专业深度但偏理论探讨，时效性一般。",[188],{"name":179,"url":177},[27,28,190,191],"基层治理","农村数字治理",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":194},{"candidates":195,"agricultural_evidence":54,"reason":55},[196,201,205],{"code":197,"score":22,"supported":14,"title":198,"abstract":199,"tags":200},"G5",[],[],[190],{"code":45,"score":22,"supported":14,"title":202,"abstract":203,"tags":204},[],[],[27],{"code":50,"score":22,"supported":14,"title":206,"abstract":207,"tags":208},[],[],[28],[210,211],"农村数字治理 新质生产力 基层治理 数字乡村","农村数字治理 新质生产力","农村数字治理新质生产力基层治理数字乡村-2613",3,{"doi":8,"openalex_id":8,"authors":215,"venue":8,"cited_by_count":120,"oa_url":8,"card":216,"direction":125,"ingested_from":127},[],{"tldr":217,"method":218,"finding":219,"direction":125,"opportunity":220},"探讨新质生产力赋能农村基层数字治理的困境与路径。","政策分析与理论探讨，无实证数据。","新质生产力可赋能农村基层数字治理，但面临现实困境需路径优化。","可结合案例或实证数据，检验新质生产力对农村数字治理绩效的影响机制。","2026-09-16T00:03:52.570396Z",{"id":223,"title":224,"url":225,"summary":226,"summary_zh":8,"content":227,"source_name":228,"source_url":8,"published_at":229,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":72,"score_detail":230,"sources":232,"tags":234,"primary_topic":240,"secondary_topics":244,"classification":245,"search_phrases":256,"slug":259,"view_count":260,"doi":8,"paper":261,"created_at":263},1002,"Digital villages and agricultural green total factor productivity: Evidence from a quasi-natural experiment in China","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37246876","原文标题: Digital villages and agricultural green total factor productivity。作者: Guo Hui、Wang Xinyi、Xia He、Jiang Wenjie。DOI: 10.3389\u002Ffsufs.2026.1831978。本文采用super-efficiency SBM-DDF-GML模型，运用2014—2023年中国县级面板数据测算AGTFP，并以国家数字乡村试点为准自然实验，运用DID方法评估数字乡村发展对AGTFP的影响、机制与空间溢出效应。研究发现：①数字乡村发展显著提升AGTFP，效应主要源于绿色技术进步而非绿色技术效率改善；②机制上通过优化劳动力配置、激发农业创业活力、扩大消费需求；③异质性上，距省会近、地形起伏度高、路网密度低的地区政策效应更显著；④空间效应以虹吸为主，但绿色技术进步随时间推移呈现溢出迹象。","## Digital villages and agricultural green total factor productivity\n\n## Guo, HuiWang, Xinyi; Xia, He; Jiang, Wenjie\n\nAngol nyelvű Szakcikk (Folyóiratcikk) Tudományos\n\n*   SJR Scopus - Horticulture: D1\n\nEnhancing agricultural green total factor productivity (AGTFP) is essential for advancing high-quality agricultural development. This study employs a super-efficiency SBM-DDF-GML model to calculate AGTFP using county-level panel data from China (2014-2023). Leveraging national digital village pilot (NDVP) programs as a quasi-natural experiment, it further applies a Difference-in-Differences (DID) approach to evaluate the impact, underlying mechanisms, and spatial spillover effects of digital village development on AGTFP. Results indicate that digital village initiatives significantly boost AGTFP. The results remain robust across multiple robustness checks and endogeneity tests, with effects primarily driven by green technological progress rather than green technological efficiency improvements. Mechanism analysis indicates that digital village development enhances AGTFP by promoting optimal labor allocation, stimulating agribusiness entrepreneurship, and expanding consumption. Heterogeneity analysis reveals that the policy effect is more pronounced in areas near provincial capitals, with high terrain undulation, and low road network density. The spatial effect during the sample period is dominated by siphoning, with only green technological progress exhibiting signs of spillover over time. This study provides new empirical evidence on how digital village development drives agricultural green transformation and offers policy implications for optimizing policy design and promoting coordinated regional development.","Frontiers in Sustainable Food Systems 2026, 10, 1831978 · 2026-08-26","2026-08-26T00:00:00Z",{"impact":18,"substance":18,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":231},"基于全国县级面板数据与准自然实验，实证数字乡村对农业绿色全要素生产率的影响，方法严谨，结论具有政策参考价值。",[233],{"name":228,"url":225},[27,29,235,236,237,238,239],"双重差分","县级面板","绿色全要素生产率","DID","Front Sustain Food Syst",{"code":45,"slug":241,"name":242,"path":243,"parent_code":87},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",[],{"status":90,"locked":14,"version":35,"method":36,"evidence":246},{"candidates":247,"agricultural_evidence":54},[248,252],{"code":45,"score":21,"supported":54,"title":249,"abstract":250,"tags":251},[],[27],[27],{"code":100,"score":22,"supported":14,"title":253,"abstract":254,"tags":255},[],[],[237],[257,258],"绿色全要素生产率 县级面板 双重差分 数字乡村","绿色全要素生产率 县级面板","绿色全要素生产率县级面板双重差分数字乡村-1002",7,{"doi":8,"openalex_id":8,"authors":262,"venue":8,"cited_by_count":120,"oa_url":8,"card":8,"direction":8,"ingested_from":127},[],"2026-08-27T00:05:20.326038Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":8,"content":269,"source_name":270,"source_url":8,"published_at":271,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":272,"score_detail":273,"sources":275,"tags":277,"primary_topic":281,"secondary_topics":282,"classification":283,"search_phrases":302,"slug":305,"view_count":213,"doi":306,"paper":8,"created_at":307},222,"数字乡村发展与农业绿色全要素生产率——来自中国的证据","https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffsufs.2026.1746394\u002Ffull","基于中国30个省份2012-2022年面板数据,构建包含数字基础设施、数字服务能力和数字发展环境的多维评价框架,采用固定效应模型、中介模型和面板阈值模型考察数字乡村发展对农业绿色全要素生产率的影响。结果表明数字乡村发展显著提升AGTFP;机制分析显示数字化通过促进劳动力流动、扩大规模化经营、加强合作社发展、加快机械化与农业研发提升绿色效率;土地流转的正面效应受制度摩擦制约;阈值分析显示数字基础设施的影响在跨越临界水平后更强。","## Abstract\n\nThis study investigates how digital rural development influences agricultural green total factor productivity (AGTFP) in China, with particular attention to stage characteristics and regional heterogeneity. Using panel data from 30 provinces from 2012 to 2022, we construct a multidimensional evaluation framework incorporating digital infrastructure, digital service capacity, and the digital development environment. A fixed-effects model is employed to estimate the overall impact, mediation models are used to examine the roles of factor allocation, organizational upgrading, and technology diffusion, and a panel threshold model is applied to identify nonlinear effects. The results show that digital rural development significantly enhances AGTFP, and this finding is robust to alternative measures, sample adjustments, and endogeneity tests. Mechanism analyses reveal that digitalization improves green efficiency by promoting labor mobility, expanding large-scale operations, strengthening cooperative development, and accelerating mechanization and agricultural R&D. However, the positive effect of land transfer remains constrained by institutional frictions, limiting its contribution to green transformation. Threshold analyses indicate that the impact of digital infrastructure becomes stronger once a critical level is surpassed, whereas the marginal effect of digital services weakens at higher stages of development. Regional heterogeneity further shows that the positive effects are most pronounced in eastern provinces and in non-grain-producing regions. Overall, digital rural development functions as a multidimensional driver of agricultural green transformation, offering empirical evidence and policy insights for designing differentiated digitalization strategies that support sustainable agricultural development.\n\n## 1 Introduction\n\nThe rapid expansion of the digital economy in recent years has substantially advanced rural digitalization in China. Nationwide, the targets of “gigabit county connections, 5G coverage in every township, and broadband access in every village” have largely been achieved. By 2024, over 90% of administrative villages had access to 5G networks, and rural internet penetration reached 63.8%. Meanwhile, digital applications—such as online public services, smart agriculture, and digital breeding—have become deeply embedded in rural production and governance. Globally, digital transformation is recognized as a key pathway toward sustainable food systems. From the perspective of the resource-based view, data have emerged as a new strategic resource that enhances productivity through digital technologies. The widespread adoption of these technologies in agriculture provides new opportunities to overcome resource and environmental constraints and to accelerate green transformation (; ).\n\nHowever, China’s progress in agricultural green development remains limited. The excessive use of fertilizers and pesticides continues to cause non-point source pollution, resulting in a relatively low level of agricultural green total factor productivity (AGTFP) (). This paradox—rapid rural digitalization yet lagging green transformation—raises a critical question: can digital rural development truly empower agricultural green transformation?\n\nPrevious studies provide preliminary evidence that rural digitalization enhances agricultural efficiency (; ; ). Yet, most research has focused on measuring the level of digital village development or assessing its direct effects on productivity, with limited exploration of the internal mechanisms and regional heterogeneity. Whether digitalization improves AGTFP depends on several empowerment channels, including the reduction of transaction costs, facilitation of factor mobility, upgrading of organizational structures, and diffusion of green technologies. These mechanisms may vary across development stages and regions due to differences in infrastructure, economic conditions, and environmental pressures.\n\nTo address these gaps, this study examines how digital rural development affects AGTFP, whether such effects are stage-dependent or region-specific, and through which mechanisms they operate. Using provincial panel data from 2012 to 2022, we integrate mediation and threshold models to identify key empowerment pathways and nonlinear characteristics. The findings provide theoretical insights and practical implications for promoting sustainable and regionally balanced agricultural digitalization.\n\n## 2 Literature review and theoretical framework\n\n### 2.1 Literature review\n\n#### 2.1.1 Digital economy and sectoral development\n\nThe digital economy, characterized by data as a key production factor and digital technologies as enabling tools, has been widely recognized as a fundamental driver of efficiency improvement and structural transformation across multiple sectors (; ). Existing studies consistently show that the digital economy reshapes traditional production and governance systems by reducing information asymmetry, optimizing resource allocation, and accelerating technological innovation, thereby exerting broad economic, environmental, and social impacts (; ; ; ). Similar cross-sectoral effects of the digital economy have also been documented in the tourism sector, where digitalization promotes growth through online attention allocation and transportation infrastructure, as well as in rural human settlement improvement, highlighting its role in enhancing living environments beyond purely economic outcomes (; ; ).\n\nFrom the perspective of environmental sustainability, a growing body of literature demonstrates that digital economy development significantly contributes to green outcomes. Empirical evidence from China indicates that the digital economy reduces carbon emissions and enhances carbon efficiency by promoting green technological innovation and upgrading industrial structure, with these effects exhibiting clear nonlinear characteristics and regional heterogeneity. Similar findings are reported in studies on energy systems, where digitalization alleviates energy poverty primarily through innovation-driven channels, and the positive effects only emerge after surpassing certain development thresholds (). These results highlight that the environmental dividends of the digital economy are neither automatic nor uniform, but depend critically on development stages and structural conditions.\n\nBeyond environmental outcomes, the digital economy has also been shown to exert profound influences on social governance and public service provision. Recent studies focusing on rural China reveal that digital economy development improves public security by increasing non-agricultural employment opportunities and household income, thereby indirectly enhancing social stability (). In the public health domain, interprovincial evidence suggests that the digital economy significantly raises medical and health service levels through improved resource allocation efficiency and coordinated regional development, while generating notable spatial spillover effects (). Together, these findings underscore that the digital economy functions as a systemic force that reshapes factor mobility, institutional performance, and governance capacity across sectors.\n\nMoreover, quasi-natural experimental studies further strengthen the causal interpretation of these relationships. Using the establishment of big data comprehensive pilot zones as an identification strategy, recent research confirms that digital economy development promotes high-quality economic growth through human capital accumulation and green technological innovation, with pronounced heterogeneity across regions, city sizes, and development stages (). This strand of literature emphasizes that the impacts of the digital economy are mediated by internal mechanisms and conditioned by structural environments, rather than operating through simple linear effects.\n\nOverall, the existing evidence suggests that the digital economy is not merely a sector-specific technological upgrade, but a multidimensional development system capable of influencing economic performance, environmental sustainability, and social outcomes through resource reallocation, innovation diffusion, and institutional transformation. These cross-sectoral mechanisms, including resource reallocation, innovation diffusion, and institutional upgrading, are also highly relevant to agriculture, where factor mobility and technology adoption are central to green productivity improvements (; ; ).\n\n#### 2.1.2 Determinants of agricultural green total factor productivity\n\nAgricultural green total factor productivity (AGTFP) has been widely used as a comprehensive indicator to assess the quality and sustainability of agricultural development, as it simultaneously incorporates desirable outputs and undesirable environmental impacts such as carbon emissions and non-point source pollution (; ). A growing body of literature has examined the determinants of AGTFP, indicating that agricultural green productivity is jointly shaped by factor endowments and allocation efficiency, technological progress and diffusion, as well as institutional and environmental conditions.\n\nFirst, factor endowment structure and allocation efficiency constitute fundamental determinants of AGTFP. Existing studies suggest that labor input quality, capital accumulation, and land use efficiency are closely associated with agricultural green productivity (; ). In particular, distortions in factor allocation and barriers to factor mobility tend to reduce production efficiency while intensifying environmental pressures, thereby constraining improvements in AGTFP (). Optimizing the allocation of labor and capital across regions and sectors has therefore been identified as a critical pathway for enhancing agricultural green productivity.\n\nSecond, technological progress and the diffusion of agricultural technologies represent another key driver of AGTFP. Technological innovation not only improves production efficiency but also reduces resource consumption and pollution emissions through cleaner production methods and environmentally friendly input use (). Empirical evidence indicates that green technological innovation, mechanization, and the adoption of sustainable farming practices significantly enhance AGTFP; however, the effectiveness of technological progress depends critically on the speed and scope of technology diffusion, which varies across regions and stages of development (; ).\n\nThird, institutional arrangements, environmental regulations, and external conditions also exert significant influences on AGTFP. Climate change, environmental constraints, and policy interventions affect agricultural green productivity through both direct and indirect channels. For example, adverse climate conditions—such as rising temperatures and increased precipitation variability—have been shown to exert nonlinear and regionally heterogeneous effects on China’s AGTFP (). At the same time, appropriate environmental regulations and agricultural support policies can incentivize cleaner production behavior and stimulate green technological innovation, thereby contributing to improvements in agricultural green productivity (; ).\n\nOverall, the existing literature indicates that AGTFP is the outcome of multiple interacting forces, including factor allocation efficiency, technological progress and diffusion, and institutional and environmental conditions. This body of research provides a solid foundation for examining how new development forces may further influence agricultural green productivity and underscores the importance of exploring the mechanisms through which such influences operate.\n\n#### 2.1.3 Digital rural development and agricultural green total factor productivity\n\nBuilding on the established determinants of AGTFP discussed above, recent studies have begun to explore digital rural development as a new development force that reshapes these traditional drivers rather than replacing them (; ). As an extension of the digital economy into agriculture and rural areas, digital rural development integrates digital infrastructure, digital services, and supportive institutional environments, thereby transforming agricultural production conditions and rural factor allocation systems.\n\nExisting empirical evidence suggests that digitalization-related factors can significantly enhance AGTFP through multiple channels (; ; ; ; ). In particular, studies on digital inclusive finance show that improved access to digital financial services alleviates farmers’ credit constraints, promotes investment in green technologies, and enhances production efficiency, ultimately contributing to higher AGTFP (). Similarly, recent research demonstrates that digital finance promotes AGTFP indirectly by fostering digital village development, highlighting the mediating role of rural digital infrastructure and service capacity in transmitting digital dividends to agricultural green productivity ().\n\nBeyond financial digitalization, a growing strand of literature emphasizes the importance of comprehensive digital rural development in improving agricultural green productivity. Digital infrastructure, such as broadband networks and information platforms, reduces information asymmetry and transaction costs, thereby facilitating the more efficient allocation of labor, capital, and land across agricultural activities (; ; ). Meanwhile, enhanced digital service capacity improves farmers’ access to market information, technical guidance, and public services, which in turn promotes organizational upgrading and the adoption of environmentally friendly production practices.\n\nDigital rural development also plays a crucial role in accelerating the diffusion of green technologies and knowledge spillovers in agriculture. Through digital platforms and information networks, advanced agricultural technologies and sustainable farming practices can be disseminated more rapidly across regions, shortening the lag between innovation and adoption. This process not only enhances productivity but also mitigates environmental pressures by encouraging cleaner production methods and more efficient input use.\n\nTaken together, existing studies suggest that digital rural development affects AGTFP primarily by reshaping factor allocation, facilitating organizational upgrading, and accelerating the diffusion of green technologies, although the strength and timing of these effects depend on regional development conditions and infrastructure endowments. In regions with insufficient digital infrastructure or limited human capital, the positive impacts of digitalization on agricultural green productivity may be constrained or delayed, implying the presence of nonlinear characteristics and regional heterogeneity.\n\nDespite these contributions, existing studies exhibit several important limitations. First, much of the literature focuses on single dimensions of digitalization, such as digital finance, rather than conceptualizing digital rural development as an integrated system encompassing infrastructure, service capacity, and the broader development environment. Second, the internal mechanisms through which digital rural development influences AGTFP, particularly factor allocation, organizational upgrading, and technology diffusion, remain insufficiently examined. In addition, most existing studies rely on linear specifications and thus fail to capture potential threshold effects or stage-dependent dynamics, which may obscure the conditional nature of digital rural development’s impact on agricultural green productivity. In particular, when digital infrastructure and service capacity remain below critical levels, the enabling effects of digital rural development may not fully materialize. Accordingly, this study conceptualizes digital rural development as a systemic and conditional driver of agricultural green transformation rather than a technological input. By constructing a multidimensional digital rural development index and empirically examining its effects on AGTFP through mechanism analysis, threshold modeling, and regional heterogeneity analysis, this study seeks to extend the existing literature and provide more nuanced evidence on how digital transformation contributes to sustainable agricultural development.\n\n### 2.2 Theoretical analysis and research hypotheses\n\n#### 2.2.1 Overall effect of digital rural development on agricultural green total factor productivity\n\nDigital rural development—anchored in digital infrastructure, service capacity, and the development environment—facilitates the transformation and upgrading of agricultural production through multidimensional coordination, thereby enhancing agricultural green total factor productivity (AGTFP). The construction of digital infrastructure enhances information acquisition and transmission efficiency, reduces transaction costs in production and circulation, and improves resource utilization (; ). Enhanced digital service capacity allows farmers easier access to markets and policy resources, strengthening operational flexibility and supporting the transition toward precision and green production (). Meanwhile, a supportive digital development environment—characterized by stable energy supply, efficient transport, and industrial linkages—creates favorable conditions for applying digital tools and disseminating green practices (; ).\n\n> _H1_: Digital rural development significantly enhances agricultural green total factor productivity.\n\n#### 2.2.2 Indirect effects: mediation mechanisms\n\n##### 2.2.2.1 Factor allocation efficiency\n\nInstitutional economics posits that transaction costs and information asymmetry hinder the free flow of production factors (). By advancing information infrastructure and e-commerce platforms, digital rural development improves the allocation efficiency of land and labor. The expansion of internet access and digital platforms reduces transaction costs in land transfers (), promotes off-farm employment, and facilitates large-scale operations. Transparent digital information also alleviates asymmetry, improving market matching. Furthermore, digital platforms create new employment and training opportunities, optimizing labor allocation and human capital utilization, thereby enhancing resource efficiency and AGTFP.\n\n> _H2_: Digital rural development indirectly promotes AGTFP by improving the allocation efficiency of key factors such as land and labor.\n\n##### 2.2.2.2 Organizational upgrading\n\nAccording to collective action theory, the fragmented nature of smallholders hinders coordination in green agricultural production (). The rise of digital platforms fosters collaboration among cooperatives, family farms, agribusinesses, and individual farmers (,; ; ). These new organizational forms improve market access, information sharing, and resource integration while facilitating the collective adoption of green technologies and standardized production practices. Rural e-commerce cooperatives that leverage big data and logistics systems exemplify how digitalization enhances operational efficiency and reduces resource waste. Consequently, organizational upgrading strengthens specialization, fosters economies of scale, and accelerates the adoption of green technologies.\n\n> _H3_: Digital rural development indirectly promotes AGTFP by fostering organizational upgrading and large-scale agricultural operations.\n\n##### 2.2.2.3 Technology diffusion\n\nInnovation diffusion theory emphasizes that the adoption of new technologies depends on effective information channels and social acceptance (). Digital rural development accelerates the dissemination of green and smart agricultural technologies by enhancing connectivity and service systems. Supported by public policies and socialized service platforms, technologies such as IoT-based monitor","Frontiers in Sustainable Food Systems 2026-03-17","2026-03-17T00:00:00Z",74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":60,"relevant":22,"comment":274},"基于省级面板数据的实证研究，揭示数字乡村对农业绿色全要素生产率的提升机制与区域异质性，结论可靠，但发表时间较早，时效性不足。",[276],{"name":270,"url":267},[27,278,279,29,280],"Frontiers","农业绿色全要素生产率","阈值模型",{"code":45,"slug":241,"name":242,"path":243,"parent_code":87},[],{"status":90,"locked":14,"version":35,"method":36,"evidence":284},{"candidates":285,"agricultural_evidence":54},[286,290,296],{"code":45,"score":20,"supported":54,"title":287,"abstract":288,"tags":289},[27],[27],[27],{"code":291,"score":213,"supported":54,"title":292,"abstract":293,"tags":295},"F5",[],[294],"机械化",[],{"code":297,"score":213,"supported":54,"title":298,"abstract":299,"tags":301},"G2",[],[300],"土地流转",[],[303,304],"农业绿色全要素生产率 数字乡村 阈值模型 Frontiers","农业绿色全要素生产率 数字乡村","农业绿色全要素生产率数字乡村阈值模型Frontiers-222","10.3389\u002Ffsufs.2026.1746394\u002Ffull","2026-08-04T23:54:47.709265Z",{"id":309,"title":310,"url":311,"summary":312,"summary_zh":8,"content":313,"source_name":314,"source_url":8,"published_at":315,"category":316,"cover_url":8,"hotness":13,"is_selected":14,"score":317,"score_detail":318,"sources":322,"tags":324,"primary_topic":8,"secondary_topics":327,"classification":328,"search_phrases":344,"slug":347,"view_count":260,"doi":8,"paper":8,"created_at":348},188,"解读丨人工智能与广袤乡村如何双向奔赴","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_1656a5f388296952","农民日报评论员文章指出:通过制度创新引导人才、资本、数据等要素向农业农村有序流动,构建多主体协同参与的良好生态,让人工智能落地生根;要做实应用场景,聚焦生产、经营、管理、服务全链条关键环节,推动技术应用从点状突破向面上铺开转变;要推进软硬并举,与农业基础设施改造、装备水平提升、流通体系优化等系统性工程统筹规划、一体推进,以软件赋能流程再造,以硬件升级支撑效能释放,形成软硬协同、迭代跃升的良性格局。","云图之上，“妈祖”预警横跨山海，风雨信息分秒必达；育种田间，“吉儿”精准授粉，数据回传种质库；手机屏里，“神农”随问随答，老经验秒变金处方……人工智能正以超乎想象的速度融入农业农村，让这片古老的土地焕发出前所未有的生机。\n\n近日，在2026世界人工智能大会暨人工智能全球治理高级别会议开幕式上，国家主席习近平提出：“要抓住难得的历史性机遇，鼓励开源开放、合作共享，全面促进人工智能科技创新、产业发展、场景应用，协同推进传统产业改造升级、新兴产业培育壮大、未来产业前瞻布局，让人工智能赋能千行百业。”\n\n当前，百年变局加速演进，人工智能技术创新进入前所未有的活跃期。放眼全球，科技实力已成为衡量国家综合国力的核心要素，人工智能作为引领新一轮科技革命与产业变革的战略性技术，具有极强的“头雁”效应，谁能在这一领域抢占先机，谁就能在国际舞台上掌握更多话语权。聚焦国内，人工智能更是推动高质量发展、实现中国式现代化的关键引擎。从“中国制造”向“中国智造”的跃升，到医疗、养老等民生领域的持续创新，人工智能正深度融入经济社会各领域。而“三农”作为国家发展的“压舱石”，既是智能化转型的重要战场，也是检验技术温度与治理水平的试金石。\n\n事实上，人工智能在为农业农村注入现代化“新动能”的同时，也在被“三农”的独特性反向塑造着技术发展路径。一方面，小农户与分散化经营决定了算法必须适应“碎片化”场景，这对数据采集精度与模型泛化能力提出更高要求；另一方面，乡村数字基建薄弱、留守群体老龄化等现实约束，也要求人工智能要学会听懂乡音、看懂乡土，倒逼智能终端向简易化与低成本方向迭代。","农民日报 2026-08-01","2026-08-01T00:00:00Z","报道",77,{"impact":319,"substance":19,"depth":320,"authority":20,"freshness":260,"relevant":22,"comment":321},25,15,"央媒报道，结合国家领导人讲话，探讨AI与三农双向奔赴，具有全国性影响，内容有实质分析，但时效性稍差。",[323],{"name":314,"url":311},[27,28,325,326],"人工智能+农业","评论",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":329},{"candidates":330,"agricultural_evidence":54,"reason":55},[331,336,340],{"code":332,"score":22,"supported":14,"title":333,"abstract":334,"tags":335},"I2",[],[],[325],{"code":45,"score":22,"supported":14,"title":337,"abstract":338,"tags":339},[],[],[27],{"code":50,"score":22,"supported":14,"title":341,"abstract":342,"tags":343},[],[],[28],[345,346],"人工智能+农业 新质生产力 数字乡村","人工智能+农业 新质生产力","人工智能+农业新质生产力数字乡村-188","2026-08-04T01:15:42.970577Z"]