[Significance] Smart agriculture represents a strategic choice for addressing the global challenges of food security, climate change, and an aging agricultural labor force, and for cultivating new-quality productive forces in agriculture. Shaped by factors such as factor endowments, institutional traditions, and value preferences, economies have not converged upon a single optimal path, but have instead formed highly heterogeneous practical configurations across driving actors, institutional environments, technological pathways, and value orientations. Existing research remains largely confined to the parallel enumeration of country-specific experiences and lack a unified comparative framework; their mechanistic explanations seldom move beyond the single dimension of factor endowments, leaving the multidimensional, co-evolutionary dynamics inadequately understood. In view of this, the archetypal models of global smart agriculture were systematically compared, the mechanisms underlying their divergence were uncovered, and their evolutionary trends were assessed, so as to provide theoretical references and practical guidance for China to build an autonomous, controllable, and openly collaborative smart agriculture system and in advancing from technological follower to co-builder of rules. [Progress] Drawing on the theory of induced innovation and a comparative institutional analysis perspective, a four-dimensional analytical framework of "driving actors-institutional environment-technological pathway-value orientation" was constructed, and five representative economies, the United States, the European Union, Japan, Israel, and China, were selected to distill five archetypal models. The commercialized large-scale farm model exemplified by the United States is one in which the private sector leads and releases economies of scale through large-scale field operations. The green and intensive model exemplified by the European Union is one in which intensive production is steered by binding green constraints. The refined collaborative model exemplified by Japan is one in which platform-based co-governance alleviates super-aging and smallholder fragmentation. The water-saving innovative model exemplified by Israel is one in which a full-chain water-saving technology system has developed under acute water scarcity. The integrated collaborative model exemplified by China is one in which national strategy integrates multiple actors, coupling the 'large-country-with-smallholders' condition with leading digital infrastructure such as BeiDou, 5G, and AI. On this basis, four mechanisms, induced innovation, path dependence, the antitrust tradition, and rule-based contestation, were introduced to provide a process-tracing account of model divergence in terms of sequential triggering and tension-laden coupling, the underlying drivers of this divergence were traced across the three dimensions of endowment constraints, organizational systems, and value hierarchies, and thereby the distinct value sequences of the five models were identified. Five dimensions of global evolutionary trends were assessed: technology, organization, data, value, and governance. Technologically, from digital precision operations toward intelligent autonomous decision-making. Organizationally, from single-actor-driven development toward open-ecosystem co-governance. In data terms, from isolated silos toward cross-border interoperability and factor marketization. In value terms, from a singular pursuit of yield growth toward green, low-carbon, and resilience-oriented goals, and in governance, from national fragmentation toward global regulatory coopetition. [Conclusions and Prospects] Model divergence constitutes a stable equilibrium under the multiple coupling of factor endowments, institutional structures, and historical paths, and no universally optimal model exists; beneath this divergence lies a convergent core in which data serve as a factor of production, models as the decision-making engine, ecosystems as the organizational form, and sustainability as the value baseline. Looking ahead, global smart agriculture should evolve along the trajectory of technological inclusiveness, ecological openness, value pluralism, and governance synergy. China is well positioned to assume the composite role of technology provider, institutional experimenter, and rule co-builder, working with other nations to advance global smart agriculture toward a more inclusive and symbiotic configuration.

[Significance] As an integration of digital and intelligent technologies, digital-intelligent technology serves as the core engine for agricultural goals. In the current era of digitalization and intelligence, rapid technological iteration and the expansion of application scenarios reinforce each other. Therefore, leveraging digital-intelligent technology to empower agriculture is a strategic necessity to align with the digital age trends and fully realize the new connotations of smart agriculture. Specifically, driven by data as a new production factor, digital-intelligent technology is profoundly optimizing the entire agricultural production and management process. The main objective of this study is to systematically analyze the underlying operational logic of how digital-intelligent technology enables high-quality agricultural development, comprehensively review its current application progress, objectively identify the main challenges, and actively explore practical pathways for advancement. [Progress] Based on the theory of transforming traditional agriculture and the theory of agricultural technology diffusion, this paper analyzes the underlying logic of how digital-intelligent technologies empower high-quality agricultural development. Building upon this foundation, it systematically reviews the practical applications of digital-intelligent technologies in production processes, factor allocation, management services, and supply chain systems, and reveals a clear staged evolutionary feature across domains. As described below: (1) Production processes: Evolution from single-point efficiency gains to closed-loop, whole-process intelligent decision-making. (2) Factor allocation: Shift from reliance on traditional physical resources to deep value mining of data. (3) Management services: Transition from fragmented tool applications to integrated, platform-based services. (4) Supply chain systems: Evolution from a linear chain structure to a collaborative and resilient network ecosystem. Despite this progress, it still faces challenges such as core technology bottlenecks, fragmented industrial ecosystems, data governance and security issues, difficulties in technology promotion and adoption, a weak talent support system, and uneven regional development. These challenges stem not only from the limitations of the technological development stage but also from structural contradictions such as a weak industrial foundation, complex application scenarios, and inadequate systemic coordination. Together, these factors block the large-scale adoption and full value realization of digital-intelligent technology in agriculture. [Conclusions and Prospects] To promote high-quality agriculture development empowered by digital-intelligent technology, it is necessary to adhere to systematic thinking and implement a multi-path coordinated strategy. The key focuses for future work are as follows: (1) Focus on the independent RD of key core technologies and carry out adaptive innovation for specific agricultural scenarios. (2) Accelerate the construction of a standardized, open, and interconnected technology and industrial ecosystem. (3) Establish and improve a data governance system covering data ownership definition, circulation and transaction, and security protection. (4) Explore sustainable business models and precise and effective policy support mechanisms. (5) Strengthen the cultivation of a multi - tiered digital agriculture talent pool involving RD, application, and promotion. (6) Implement regionally differentiated and coordinated development strategies according to local conditions. Through multi-stakeholder collaboration among governments, industries, research institutions and market entities, as well as integrated policy support, digital-intelligent technology will be promoted to transform from "potted landscape" style isolated demonstrations to "open landscape" style large-scale popularization and deep integration. This will inject a stronger and more sustainable digital-intelligent driving force into Chinese-style agricultural modernization and high-quality agricultural development.

[Objective] This study addresses regional disparities and imbalanced drivers in developing smart agriculture, a core approach to fostering new quality agricultural productivity. It aims to: (1) construct a comprehensive evaluation system incorporating geographical and industrial heterogeneity; (2) empirically analyze the synergistic drive between data elements and agricultural physical capital; and (3) reveal their role in inter-provincial spatial linkages. The significance lies in its potential to inform strategic planning and policy-making, thereby contributing to the sustainable transformation of agriculture and balanced regional development within the context of rural revitalization. [Methods] A quantitative spatial econometric approach was employed using panel data from 30 Chinese provinces spanning from 2015 to 2023. The research was executed in three key stages. First, a comprehensive provincial-level smart agriculture development index was constructed. This index integrated multiple dimensions and was weighted by combining the Analytic Hierarchy Process and the Entropy method, with adjustments made for terrain and leading industry heterogeneity. Second, a series of econometric models were specified. Baseline fixed-effects and generalized method of moments models were used to examine the driving role of data elements, while mediating and moderating effect models were employed to systematically verify the synergistic mechanism between data and physical capital and its pathways. Third, spatial autocorrelation tests and spatial Durbin models were employed with three spatial weight matrices—geographical contiguity, agricultural resource zoning, and agricultural economic structure similarity—to identify spatial correlation characteristics and spillover patterns. Direct and indirect effects were decomposed to precisely quantify local impacts and spatial spillovers. [Results and Discussions] The analysis yielded four clusters of key findings that confirmed and refined the proposed core hypotheses. 1) Gradient development and regional mismatch: Smart agriculture development exhibited a pronounced "ladder-like" spatial pattern. The Huang-Huai-Hai region remained the persistent leader in absolute development level. The southwestern region also maintained a relatively high level, which appeared partly "forced" by its challenging terrain, necessitated efficiency-seeking technology. In contrast, the Northeast and Northwest regions lagged. A critical systemic mismatch was revealed: Regions with the highest growth momentum were not necessarily those with the highest current smartization levels, indicating divergent developmental pathways. 2) The core synergistic mechanism: A significant positive interaction was found between data inputs and agricultural physical capital. Crucially, the independent coefficients of each were often found to be insignificant or even negative in spatial models, which underscored that limited or even negative marginal returns were yielded by isolated, uncoordinated investment in either domain. Significant systemic gains were unlocked precisely by their structural complementarity. Furthermore, this synergy was found to operate partially through the channel of technological capital accumulation. The mediating effect of technological capital was confirmed to be significant, and its own impact on smart agriculture output was exhibited as a nonlinear threshold characteristic. This confirmed that a critical mass of technological capital had to be accumulated before its benefits could be fully realized. 3) Competition-dominated spatial interactions: The spatial analysis revealed that inter-provincial dynamics were characterized primarily by competition rather than cooperation. A significant negative spatial spillover was detected specifically under the economic structure similarity matrix. This indicated that resources, talent, and investment were competed for by provinces with similar agricultural economic profiles, potentially hindering each other's growth. However, a nuanced finding was observed: while raw data or capital might be siphoned away, significant positive spatial spillovers were generated by successful provincial models of "data-capital" synergy. This suggested that best practices and development paradigms could be diffused, offering a pathway to transcend pure competition. 4) The effectiveness of the core drivers was found to be highly context-dependent. From a zoning perspective, the Huang-Huai-Hai region was characterized by digital-drive but was found to lack deep synergy; the Northeast was constrained by traditional path dependency; the Southwest was shown to exhibit a paradox of high knowledge spillover but low local application; and the northwest was characterized by singular, weak drivers. From a developmental stage perspective, the role of RD investment was assessed as stable, while the payoff from digital infrastructure was seen to be contingent on an "efficiency threshold", and its spatial spillover effect was observed to diminish as regional total factor productivity increased. [Conclusions] It is demonstrated that development is not merely a function of increased inputs, but is critically determined by the structural coupling of data and physical capital. This coupling is facilitated by technological capital, which acts as a nonlinear mediator. At a practical level, a one-size-fits-all approach to investment policy is argued against. For leading regions such as the Huang-Huai-Hai, policy focus should be placed on deepening existing synergies. For regions like the northeast, breaking path dependence is seen to require policies that forcefully couple new digital tools with legacy physical assets. The pervasive "siphon effect" is identified as necessitating national-level coordination mechanisms among structurally similar provinces to mitigate destructive competition. Ultimately, the promotion of smart agriculture is concluded to require spatially differentiated policies that strategically foster local factor synergy while managing the competitive externalities inherent in regional linkages.

[Significance] The adoption of smart agricultural technologies has significantly accelerated the development of modern agriculture, emerging as a pivotal driver for green transition within the agricultural sector. By enabling a fundamental shift in agricultural production paradigms, these innovations not only enhance desirable agricultural outputs but also effectively reduce undesirable environmental outputs. This dual effect substantially contributes to the improvement of agricultural green total factor productivity (AGTFP). [Progress] A systematic investigation was undertaken into the pivotal role of smart agriculture technology innovation in driving the green transition within the agricultural sector. The analysis was meticulously structured across three sequential stages: Firstly, it elucidated the intrinsic mechanisms through which technological advancements serve as enablers for this green transition, detailing how innovations in agriculture technology translate into environmental sustainability gains. Secondly, it identified and categorized the existing implementation barriers that hinder the widespread adoption of smart agriculture solutions, providing insights into the challenges faced by stakeholders at various levels. The advancement of smart agricultural technologies facilitated a paradigm shift, moving from isolated, fragmented data systems to cohesive, interconnected ecological systems that support sustainable growth. The core technological logic underpinning this transition operates through a synergistic "data-algorithm-equipment-ecology" evolutionary trajectory: (1) Data infrastructure construction: Establishing comprehensive, multi-source heterogeneous agricultural data acquisition systems that collect, process, and analyze data from diverse sources, forming the foundational data assets necessary for the development and deployment of smart agriculture solutions. (2) Algorithmic decision-making: Developing sophisticated AI-driven resource allocation models that leverage big data analytics to enable precision management, optimizing resource use, and enhancing productivity through intelligent decision-making systems. (3) Equipment modernization: Creating technical closed loops characterized by a virtuous cycle of "equipment upgrading-efficiency enhancement-cost reduction-environmental benefits" through the implementation of automated production management and intelligent harvesting systems that minimize human intervention and maximize output. (4) Ecological optimization: Building circular systems for waste valorization, carbon footprint tracking, and ecological monitoring that facilitate the recycling of agricultural by-products, reduce greenhouse gas emissions, and promote the restoration and preservation of agricultural ecosystems, ultimately achieving self-regulating, resilient agricultural environments. However, the implementation process faces three categories of barriers: (1) Technological barriers: High infrastructure costs, inefficient technology commercialization, insufficient digital literacy among farmers, and inadequate full-process automation/intelligence levels constrain transformation depth. (2) Market barriers: Significant mismatches between supply-side capabilities and demand-side requirements in technology applicability. (3) Institutional barriers: Firstly, the lack of policy coordination is evident in the "prioritizing construction over operation" approach, leading to many agricultural big data platforms failing to effectively implement green technologies post-completion due to inadequate policies supporting sustained operations and practical applications. Secondly, structural deficiencies in the standard system, particularly the absence of unified classification standards for agricultural information resources, create technical barriers to cross-departmental and cross-regional data sharing. [Conclusions and Prospects] To effectively advance smart agriculture technology in driving the agricultural green transition and systematically address identified barriers, the following policy recommendations are proposed: (1) To effectively overcome the persistent "last mile" barrier that impedes the widespread adoption of technological innovations among smallholder farmers, it is imperative to actively promote multi-stakeholder participation across the agricultural sector. This involves fostering collaboration among governments, private enterprises, non-profit organizations, and local communities. Additionally, establishing a comprehensive technical service network is crucial, one that seamlessly integrates government guidance to ensure policy alignment, market operations to drive efficiency and sustainability, and social engagement to enhance community buy-in and knowledge dissemination. (2) To significantly boost the efficiency of green resource allocation within the agricultural sector, it is imperative to harness the power of market-oriented reforms. These reforms should serve as the cornerstone for forging a novel supply-demand-driven model that is both dynamic and responsive. By integrating intelligent algorithms and advanced data analytics, this coordinated approach will facilitate the precise matching of essential resources such as land, capital, and cutting-edge green technologies. Such precision will enable a paradigm shift from traditional, scale-oriented production methods to more flexible, demand-driven customization tailored to specific market needs. Ultimately, this transformation will cultivate sustainable endogenous growth momentum, providing a robust foundation for the agricultural sector's green transition and ensuring its long-term viability and competitiveness. (3) Governments ought to take the lead in driving systemic and comprehensive reform of existing policy formulation frameworks, ensuring that agricultural development strategies are aligned with long-term sustainability goals. Additionally, they should strengthen technological support systems for agricultural resource zoning, leveraging advanced tools such as big data analytics and geographic information systems to optimize land use and resource allocation. By doing so, governments can foster a new universal smart agriculture development paradigm that integrates digital innovation with ecological principles. These targeted measures will not only enhance agricultural productivity but also accumulate valuable experience for comprehensively advancing the agricultural green transition and promoting rural ecological revitalization in a systematic manner.

[Objective] In recent years, China has issued a series of policy documents, which emphasize digital transformation as a key driver for deepening the integration of digital and intelligent technologies (DITs) with the agricultural and rural economy, promoting high-quality agricultural development, and advancing the goal of building a strong agricultural sector. However, the ultimate impact of these technologies depends not only on their potential effectiveness but also on the extent to which they are adopted by farmers. Against this backdrop, the aim of this research is to assess the current adoption of agricultural DITs among agricultural producers in China and to identify the main challenges facing their application in the future development of agriculture and rural areas. [Methods] Using two representative household survey datasets—the China Agriculture and Rural Development Survey and a 2023 survey of farmers in major grain-producing regions—this study employs descriptive statistics to document the adoption of various DITs. Then they were combined with a review of the literature to examine the potential impacts and underlying mechanisms of technology adoption, and to analyze the challenges and their causes in the diffusion of these technologies by linking theory with practice. [Results and Discussions] The results showed that, for non-embedded digital technologies, smartphone use among primary agricultural decision-makers was widespread, with an average penetration rate of 76%. The most frequently used applications were WeChat, TikTok, and Kuaishou. Despite the high usage of WeChat, its application in agricultural production and marketing remains limited: about two-thirds of respondents report rarely or never following agriculture-related official accounts. Similarly, access to agricultural policy information via smartphones was limited, with approximately 78% of respondents indicating that they seldom or only occasionally followed such information. In terms of e-commerce, about 52% of rural households engaged in online shopping in 2023, but very few used online channels for sales: only 0.32% sold agricultural products and 0.15% sold other goods online. In digital finance, only 27% of surveyed households were aware that loans could be applied for online, and the share that had actually applied for online loans in the past two years was even lower, at around 2%. By contrast, embedded digital technologies, such as drones and Beidou-based navigation systems, were more widely used in agricultural production. Data from nine provinces show that about 8% of farmers used drones for crop protection in 2023, reflecting a clear upward trend. In major grain-producing regions, drones covered 39.3% of cultivated land for plant protection, largely through service outsourcing. In addition, the share of farmers using machinery equipped with Beidou navigation systems, weighted by the number of households and cultivated land area, reaches 8% and 12.5%, respectively. Building on these findings, the study analyzes the relatively low adoption of non-embedded technologies in agricultural settings from the perspectives of farmer awareness, digital literacy, infrastructure, and policy support. It also explores the diffusion of embedded technologies from the angles of cost, agricultural organization (particularly the development of service providers), and industry standards. [Conclusions] Overall, China's agricultural and rural digital transformation is still at an early stage. The adoption of non-embedded applications in agricultural production and marketing remains very limited, while the use of embedded technologies exhibits substantial heterogeneity across farm sizes and application contexts. In addition, although smallholders rarely invest in digital machinery themselves, they can still benefit from digital transformation through outsourced services and socialized service provision. Further analysis suggests that advancing agricultural digitalization requires improving farmers' digital literacy, fostering new agricultural business entities with higher human capital, promoting the development of appropriately scaled farming, and strengthening the capacity of service providers. These measures are essential to enable broader and more effective diffusion of DITs and to support agricultural modernization.

[Significance] The escalating complexity of genotype-phenotype-environment interactions and the explosive growth of multi-omics big data have necessitated a paradigm shift in crop breeding from empirical selection to intelligent design (Breeding 5.0). The aim of this paper is to systematically explore the underlying logic of the AI-driven crop breeding paradigm shift, comprehensively outline its generational evolution, analyze its core technical implementations in phenomics, genomics, multi-omics integration, and molecular design, and dissect how large agricultural foundation models reconstruct the entire seed industry workflow. [Progress] The historical evolution of crop breeding was first traced from 1.0 empirical domestication to 5.0 smart Breeding characterized by the deep integration of biotechnology (BT) and information technology (IT). In germplasm resource evaluation, deep learning algorithms enabled high-dimensional pattern recognition and unsupervised feature compression to unlock rare alleles from unannotated sequences, large language models (LLMs) like PlantConnectome and wheat germplasm information extraction (WGIE) leverage retrieval-augmented generation (RAG) to automatically construct structural knowledge graphs from unstructured historical literature, achieving predictive and dynamic germplasm evaluations. In high-throughput phenotyping, industrial platforms capture 3D point cloud and multi-spectral data at 0.1 mm resolution, while convolutional neural networks couple with the integrated genomic-enviromic prediction (iGEP) framework to build full-lifecycle digital twin crop models in virtual space. Regarding genomic prediction, the limitations of linear paradigms were dissected and cutting-edge deep learning architectures were highlighted: SoyDNGP applied a 3D-CNN to map chromosomal topology for complex soybean traits; DPCformer employed self-attention mechanisms to dynamically calculate environmental weights under multi-adversarial constraints; and HyenaDNA utilized long-convolution filters to bypass the O(N²) computational complexity limitation of standard Transformers, reducing it to O(N·log N) for chromosome-scale modeling. For multi-omics integration, intermediate fusion strategies were elucidated for their superior capacity to capture cross-layer biological compensatory pathways. In molecular design breeding, foundational plant language models were highlighted, such as AgroNT for zero-shot expression prediction and OpenCRISPR-1, the world's first de novo AI-generated genome editor built to capture underlying physico-chemical syntax. Furthermore, the emergence logic of major domestic and international agricultural foundation models was analyzed through the mathematical lenses of scaling laws, parameter-efficient fine-tuning, and multi-task evaluation benchmarks. Finally, empirical effectiveness was comprehensively evaluated through multinational success stories, including Bayer's Climate FieldView, IRRI's night-temperature thermal models for "Green Super Rice", and China's state-led breeding platforms for stress-resistant maize and high-yield soybean. [Conclusions and Prospects] Key structural challenges that smart breeding faces are thoroughly dissected: data silos and standardization dilemmas, extreme computational resource asymmetry and high training costs, the lack of biological causal logic in deep learning "black boxes", and the structural scarcity of interdisciplinary BT-IT talent. To overcome these bottlenecks, future research and policy efforts should focus on four pillars: 1) Establishing standardized open data ecosystems following FAIR (Findable, Accessible, Interoperable, Reusable) principles and promoting federated learning under a national AI data copyright integration platform to safeguard digital borders; 2) Exploring cloud-to-edge lightweight model deployment pathways through parameter-efficient fine-tuning, quantization, and knowledge distillation to empower real-time field-side decision-making and realize compute equity; 3) Deepening the mechanistic integration of AI and synthetic biology by absorbing breakthroughs from multi-scale modeling frameworks into the virtual "design-build-test-learn" cycle to break natural evolutionary thresholds; and 4) improving regulatory approval frameworks for AI-designed crops and modernizing agricultural education to cultivate a new generation of digital agronomists.

[Objective] As agricultural modernization accelerates, intelligent agricultural machinery is playing an increasingly important role in enhancing productivity and ensuring food security. Among these technologies, BeiDou navigation tractors have shown considerable potential in improving operational precision and efficiency. However, their adoption among farmers remains limited, and large-scale diffusion has yet to be achieved. Understanding what drives or constrains farmers' adoption decisions is therefore critical for promoting the effective use of intelligent agricultural machinery. The aim of this research is to identify the key determinants of farmers' adoption behavior and to uncover the underlying mechanisms shaping these decisions. [Methods] Micro-survey data collected from 1 242 maize farmers across six provinces in China were used as the basis. An analytical framework was constructed from five dimensions, including household head characteristics, family endowments, operational conditions, regional development, and the external environment. Explainable machine learning methods were employed to identify the key driving factors influencing farmers' adoption of BeiDou navigation tractors. [Results and Discussions] The results showed that farmers' adoption decisions regarding BeiDou navigation tractors were characterized by the dominance of a small number of core factors. Among these, policy support for intelligent agricultural machinery, total household income, farm size, age, and participation in agricultural training ranked as the top five determinants. This indicated that policy incentives, economic capacity, land endowments, and human capital were the primary factors influencing farmers' adoption decisions. In terms of the direction of influence, stronger human, physical, and social capital effectively reduced the barriers to technology adoption. The consolidation and contiguity of farmland, together with the application of complementary technologies, significantly improved the suitability of BeiDou navigation tractors. In addition, a well-developed policy support system and agricultural socialized service network created favourable conditions for adoption. The heterogeneity analysis further revealed that smallholder farmers relied more on demonstration effects and external service support, whereas large-scale farmers' adoption decisions were more strongly driven by policy incentives and economic returns. Younger farmers exhibited adoption behaviour that was more evidently driven by economic and policy factors, while older farmers showed greater dependence on services and a stronger tendency towards risk aversion. Farmers operating under less favourable terrain conditions were more influenced by resource endowments and policy support, whereas those in more favourable conditions were more driven by access to services and human capital. [Conclusions] Based on these findings, it is suggested that promoting the large-scale adoption of intelligent agricultural machinery requires improvements in policy support. First, the focus should shift from hardware subsidies to performance enhancement. On the one hand, differentiated subsidy policies should be implemented. On the other hand, subsidy mechanisms should be extended from equipment purchase to operational use. Second, efforts should be made to promote moderate-scale farmland management in order to improve the conditions for applying intelligent agricultural machinery. This includes facilitating land transfer and service arrangements such as land trusteeship to encourage land consolidation and contiguity, thereby enabling the precision operation advantages of BeiDou navigation tractors to be fully realised. At the same time, the coordinated application of agricultural machinery and agronomic practices should be promoted. Under suitable conditions, integrating BeiDou navigation tractors with high-density planting techniques can further enhance their cost-saving and efficiency-improving potential. Finally, it is necessary to improve the agricultural socialized service system and training framework, and to implement differentiated promotion strategies for different groups. On the one hand, greater support should be given to agricultural machinery cooperatives and agricultural service organisations to provide outsourced services such as ploughing, planting, and land management for smallholders. On the other hand, more digital skills and practical training resources should be directed towards large-scale farmers, alongside technical guidance on equipment maintenance and data management, so as to better utilise their leading role in the adoption and diffusion of intelligent agricultural machinery.

[Objective] Plant factories with artificial lighting (PFALs) provide year-round production, controllable environments, consistent product quality, and high space-use efficiency, positioning them as an important form of controlled-environment agriculture (CEA) moving toward intensification and digitalization. However, their commercialization has been constrained by high capital investment, electricity dependence, labor and operation-and-maintenance costs, and insufficient realization of market value. Existing studies have typically examined crop yield, light-environment control, energy use, capital cost, or market price in isolation, with limited integration of crop production, control maturity, energy conditions, and market realization into a computable framework. The aim is to identify the profitability boundary of PFAL lettuce, clarify how control maturity affects marketable yield, unit electricity use, labor substitution, annualized capital cost, and unit cost, and identify feasible transition pathways. [Methods] A production-side accounting boundary was adopted. Annualized capital cost, maintenance cost, electricity cost, labor cost, nutrient solution and seed costs, and other operating costs were included, whereas cold-chain logistics, retail terminal costs, brand advertising costs, financing costs, and complete channel-organization costs were excluded. Three levels of control maturity, three energy scenarios, and three market scenarios were specified, resulting in 27 deterministic scenarios. The model was constructed following the logic of "scenario input – control mapping – cost – benefit calculation – profitability boundary identification – vulnerability diagnosis". Control maturity was incorporated into the profit function through gross yield, marketable rate, unit electricity consumption per unit of marketable product, labor-substitution coefficient, and unit capital expenditure (CAPEX). An economic vulnerability index (EVI), consisting of profit gap, energy exposure, and carbon-constraint exposure, was further constructed. Local elasticity analysis, weight-robustness tests, and extended scenarios involving policy support and channel costs were used to examine the explanatory boundary of the results. [Results and Discussions] The economic feasibility of PFAL lettuce exhibited a distinct "narrow-window" characteristic. Among the 27 deterministic scenarios, only 5 achieved positive profit, accounting for 18.5%, and all were concentrated in the high-value direct-supply market. Under the benchmark scenario of "conventional grid electricity + high-value direct-supply market", upgrading control maturity from basic control to closed-loop intelligent control increased marketable yield from 70.40 to 109.25 kg/(m²·year), reduced unit electricity consumption from 12.0 to 8.4 kWh/kg, decreased unit cost from 27.67 to 19.26 CNY/kg, and increased profit from -258.36 to 518.04 CNY/(m²·year). Cost decomposition showed that although control upgrading increased annualized capital cost per unit area, higher output diluted capital cost per unit product. Meanwhile, improved labor substitution and reduced unit electricity consumption lowered labor cost and electricity cost, respectively. Break-even analysis indicated that higher control maturity flattened the break-even boundary between selling price and electricity price, reflecting lower sensitivity to electricity price fluctuations. The EVI results further showed that profitability ranking and vulnerability ranking were not fully consistent. The best scenario was "closed-loop intelligent control + energy-abundant condition + high-value direct-supply market", with an EVI of 0.088, whereas the worst scenario was "basic control + high-price and high-carbon electricity condition + conventional fresh-food market", with an EVI of 0.727. Local elasticity analysis showed that marketable yield had the largest effect on unit cost, with an elasticity of approximately -0.57, followed by unit CAPEX at approximately 0.49. The elasticities of electricity price and unit electricity consumption were both approximately 0.33. Sensitivity analysis of policy support and channel costs showed that investment subsidies and preferential electricity prices improved the financial performance of some boundary scenarios, whereas additional costs associated with packaging, fulfillment, channel maintenance, and sales organization compressed profit margins in high-value markets. [Conclusions] The feasibility of PFAL lettuce production is not determined by single-factor cost reduction, but by the joint effects of control maturity, energy conditions, market value realization, and channel costs. Conventional fresh-food markets and general premium-brand markets are unlikely to support profitable PFAL lettuce production. Only in high-value direct-supply markets, and when the control level reaches at least the enhanced-control stage, can the system cross the break-even line. The value of control upgrading should not be understood merely as electricity saving, but as a comprehensive mechanism that simultaneously increases marketable yield, improves the marketable rate, reduces unit electricity consumption, enhances labor substitution, and dilutes capital cost. A more robust transition pathway should therefore be built on the synergy among control upgrading, favorable electricity conditions, value-chain upgrading, and policy support. The conclusions of this study are applicable to production-side boundary identification under publicly available data conditions, but should not be directly interpreted as evidence of stable profitability for specific commercial projects.

[Significance] Since the establishment of the "dual carbon" goals, promoting the low-carbon transformation of lifestyles has become a core issue of green and high-quality development. However, most existing studies selected urban residents as the research subject for investigating low-carbon behaviors, systematic exploration from the perspective of rural residents needs to be supplemented. The implementation of the Digital Rural Development Strategy creates favorable conditions for digital empowerment in the low-carbon transformation of rural residents' lifestyles. Based on the perspective of rural residents' behavior, this paper systematically sorts out relevant literature, aiming to explore the core difficulties of rural residents' lifestyle low-carbon transformation and the feasible paths of digital empowerment, so as to provide theoretical reference and practical guidance for the deepening of research in this field and the optimization of relevant policies. [Progress] Firstly, the mainstream theories and research findings were reviewed on resident behavior, concluding that existing studies mostly focus on urban residents, with insufficient relevance analysis and in-depth discussion in rural contexts. Secondly, the core dilemmas in the low-carbon lifestyle transformation of rural residents were analyzed from the two dimensions of behavior and cognition. At the behavioral level, rural residents have low acceptance of new low-carbon practices; their behavioral decisions are strongly influenced by social relations such as regional and kinship ties, and external strong stimuli are needed to drive behavioral change. At the cognitive level, rural residents have limited exposure to low-carbon knowledge, which hinders the formation of low-carbon awareness. Meanwhile, there is a significant deviation between low-carbon cognition and actual behavior, and more external incentives are required for rural residents to translate awareness into action. The proposal of the Digital Countryside has provided hardware infrastructure and policy support for digitally driving the low-carbon transformation of rural residents' behavior. Accordingly, the common enabling scenarios of digitalization were further sorted out in rural life and explored feasible paths for the low-carbon transformation of rural residents' behavior in the digital era. From one aspect, embedding digital technologies and their derivative products is a measure to reshape residents' living habits. Specifically, digital technologies can optimize residents' daily life experience and improve efficiency, while digital financial products and services can lower the costs of low-carbon practices for rural residents. From another aspect, leveraging digital platforms as communication channels helps gradually cultivate residents' low-carbon awareness. This is reflected in the efficient dissemination of information that expands rural residents' scope of low-carbon knowledge, and digital social interactions that transform their traditional mindsets. Finally, China's current policy system related to guiding residents' low-carbon behavior was reviewed and evaluated. It is characterized by national macro-plans that set the overall development direction, and local policies that focus on strengthening digital infrastructure construction. [Conclusions and Prospects] Digitalization can accurately address the behavioral and cognition difficulties in the low-carbon transformation of rural residents' lifestyles through four paths: technology embedding, financial empowerment, information dissemination, and social guidance. Future research needs to further expand the applicability of low-carbon behavior theories in rural scenarios, verify the carbon reduction effect and heterogeneous impact of digitalization in rural living scenarios, and construct a two-way interactive digital carbon reduction policy system featuring government guidance and resident participation, so as to provide more targeted theoretical support and policy ideas for the coordinated development of rural residents' lifestyle low-carbon transformation and rural revitalization.

[Significance] Digital literacy is essential for farming and herding households to integrate into digital financial markets and access credit services. Against the backdrop of the rapid development of inclusive digital finance, a systematic review of existing research on the relationship between digital literacy and the borrowing behaviour of farming and herding households is significant for deepening rural financial theory, revealing the mechanisms underlying the digital divide and formulating targeted digital financial policies for rural and pastoral areas. This paper focuses on farming and herding households, emphasising that, in a fintech-driven environment, digital literacy influences access to financing, economic resilience, and sustainable development capabilities, and is not merely a matter of technological adoption. [Progress] The concept of digital literacy had undergone a progressive evolution from information literacy, internet literacy, and media literacy to digital literacy, gradually forming a comprehensive competency framework that encompassed multiple dimensions, including information retrieval, communication and collaboration, content creation, and security. About measurement, the EU's DigComp 2.2 and UNESCO's DLGF had been adopted extensively. Research had constructed indicator systems across multiple dimensions, including general, social, creative, security, and financial aspects. However, the adaptability of existing measurement tools in pastoral contexts remained insufficient. Regarding the loan behaviour of farming and herding households, extant research was found to focus primarily on financing accessibility, the "last-mile" bottlenecks in rural finance, and the role of digital literacy in promoting digital financial services. The accessibility of financing was influenced by a multitude of factors, including individual and household characteristics, social capital, the policy environment, and technological development. Rural financial bottlenecks were identified as the result of several challenges, such as poor institutional transmission, insufficient technological adaptation, lack of resource guarantees, and mismatched service design. Digital literacy was demonstrated to enhance the availability of both formal and informal credit for farming and herding households by means of improving information access efficiency, expanding social networks, increasing credit visibility, and optimising risk identification. It was also shown to play a positive role in promoting income growth, technology adoption, and economic resilience. [Conclusions and Prospects] The extant research was found to confirm the positive impact of digital literacy on the loan-taking behaviour of farming and herding households. However, significant limitations remained to be addressed. Firstly, the research perspective had been primarily focused on agricultural areas and farming households, with a severe lack of specialised studies on pastoral areas and herding households, thereby overlooking the unique circumstances of pastoral areas, such as the seasonal nature of production, the biological nature of assets, and the dispersed nature of settlements. Secondly, digital literacy measurement tools were inadequately adapted to pastoral contexts, failing to cover core pastoral production scenarios such as pasture monitoring, remote livestock management, and e-commerce sales. Thirdly, research on the underlying mechanisms remained superficial; specifically, how digital literacy influenced borrowing behaviour through intermediary pathways such as social capital, financial knowledge, and risk preference, particularly the differentiated mechanisms between formal and informal channels, had yet to be clarified. Fourthly, difficulties in data acquisition and limitations in research methods were found to have constrained the reliability of causal inferences. Fifthly, there was a mismatch between digital finance policies and the actual needs of pastoral areas. It was recommended that future research efforts concentrate on the development of a theoretical analytical framework and measurement system that was specifically tailored to the pastoral context. This was to be accompanied by the advancement of large-scale, long-term micro-surveys and the development of panel data. Researchers were also advised to actively incorporate methods such as experimental economics and machine learning into their studies. Furthermore, there was a need to deepen the analysis of stratified studies on the internal heterogeneity of herding households. The implementation outcomes of existing digital inclusive finance policies and training programmes were to be systematically evaluated. Additionally, it was considered crucial to proactively address emerging topics such as green credit, insurtech and supply chain finance. Finally, critical issues including digital risk governance and algorithmic fairness were noted as requiring due attention. This provides both theoretical support and practical guidance for ensuring that digital finance benefits a wide range of farmers and herders in a more precise and equitable manner.

[Objective] Digital rural construction has become an important strategy for promoting rural revitalization and breaking the long-standing urban-rural dual structure in China. Under the goals of common prosperity and agricultural modernization, sustained income growth for rural residents is a key policy concern. Existing studies have explored the economic effects of digital rural development, but several gaps remain. Most research focuses on a single outcome rather than jointly considering income growth and broader inclusive development. Many studies also rely on composite indices that may be endogenous to local conditions. In addition, the channels through which digital rural policies affect rural income have not been fully clarified. To address these issues, the National Digital Rural Pilot Policy was used as a quasi-natural experiment in this research to examine whether digital rural construction raises rural residents' income, the transmission mechanisms of this effect and its effect on the urban-rural income gap. [Methods] Using the county-level panel data for 727 counties in China from 2014 to 2023, the 2020 launch of the National Digital Rural Pilot Policy was treated as a quasi-natural experiment, and a difference-in-differences model was applied to identify the policy's effect on rural residents' per capita disposable income.The analysis controlled for economic foundation, fiscal expenditure, financial development, population density, savings level, industrialization, service-sector development, and education. To test robustness, parallel trend tests, placebo tests, the exclusion of special samples, controls for other concurrent policies, and propensity score matching combined with DID estimation were conducted. Mechanism tests were used to examine whether the policy works through labor allocation optimization and enhanced entrepreneurial activity, while moderating effect models assessed whether county economic foundation, digital financial inclusion, and industrialization strengthen the income-enhancing effect. [Results and Discussions] The results showed that the National Digital Rural Pilot Policy significantly increased rural residents' income. After county and year fixed effects as well as other relevant factors were controlled for, the policy led to a significant increase in rural per capita disposable income, and this finding remained robust across a series of tests. Dynamic analysis showed no significant difference in pre-policy trends between pilot and non-pilot counties, while the positive effect emerged after policy implementation and strengthened over time, indicating a sustained and cumulative policy impact. Mechanism analysis identified two main channels through which the policy promoted income growth. First, digital rural construction improved labor allocation by fostering new forms of rural economic activity, such as digital agriculture and rural e-commerce, reducing labor market frictions, and improving the matching efficiency between labor and employment opportunities. Second, it enhanced entrepreneurial activity by lowering market entry and operating costs, increasing the vitality of agriculture-related business entities, and creating more opportunities for local business development and income generation. The income-enhancing effect was more pronounced in counties with a stronger economic foundation, higher levels of digital financial inclusion, and greater industrialization. However, although the policy significantly increased rural residents' income, it did not significantly reduce the urban-rural income gap, suggesting that absolute income growth did not necessarily lead to relative distributional convergence. [Conclusions] Digital rural construction is an effective pathway for increasing rural residents' income in China. By exploiting the National Digital Rural Pilot Policy as a quasi-natural experiment, this study provides more credible evidence on the income effects of digital rural development and clarifies its main transmission mechanisms. Future policy efforts should continue to advance digital rural construction while focusing on improving labor allocation, enhancing entrepreneurial activity, and strengthening county-level supporting conditions. Greater attention should also be paid to digitally disadvantaged areas and vulnerable rural groups in order to promote the inclusive sharing of digital dividends and foster more balanced urban-rural development.

[Objective] As a key policy tool for empowering the development of the rural digital economy and broadening farmers' income channels, the rural e-commerce policy holds significant practical importance for strengthening farmers' risk resistance capabilities and improving sustainable livelihood levels. The purpose of this research is to systematically investigate the effects, mechanisms, and heterogeneous characteristics of the rural e-commerce policy on farmers' livelihood resilience, scientifically evaluate the policy implementation outcomes, and provide theoretical references and decision-making bases for optimizing the arrangement of rural e-commerce policies, improving the digital policy system benefiting farmers, and enhancing farmers' sustainable development capabilities. [Methods] Based on data from the China Family Panel Studies (CFPS) from 2012 to 2022, the National Rural E-commerce Comprehensive Demonstration Policy was treated as a "quasi-natural experiment" and a staggered difference-in-differences (DID) model was employed to empirically assess the effects, mechanisms, and heterogeneity of rural e-commerce access on farmers' livelihood resilience. To solidify causal identification, a series of robustness tests and endogeneity treatments were conducted. Addressing potential biases in staggered DID estimations, a Bacon decomposition was introduced to refine the policy effect estimates. Placebo tests were utilized to rule out interference from unobservable factors and random shocks. Simultaneously, the analysis controlled for the interference of concurrent policies such as the Information Entering Villages and Households Project, the Broadband China Strategy, and the National E-commerce Demonstration Cities, and removed special samples to purify the data. To address sample selection bias, a PSM-DID model was employed for re-estimation, matching characteristics to enhance inter-group comparability. Lagged-term regressions were introduced to mitigate reverse causality issues, clarifying the temporal relationship between the policy and farmers' internet use, thereby further strengthening the rigor of the conclusions. [Results and Discussions] It was confirmed through parallel trend tests that there were no significant differences in trends between the treatment and control groups before policy implementation, satisfying the prerequisite for quasi-natural experiment identification. Placebo tests excluded interference from random factors and omitted variables, and the Bacon decomposition refined policy effect estimates, avoiding biases from heterogeneous treatment effects. Furthermore, the analysis ruled out the confounding effects of concurrent policies and excluded specific special samples to purify the data. Addressing endogeneity concerns, the PSM-DID model mitigated sample selection bias, and lagged-term regressions dismissed potential reverse causality. Following these multi-dimensional robustness checks and endogeneity treatments, the core conclusion that the rural e-commerce policy significantly enhanced farmers' livelihood resilience remained robustly valid. Inspired by the Theory of Planned Behavior, the rural e-commerce policy influenced farmers' livelihood resilience through the synergistic effect of three core dimensions: cognitive, social, and skills. Cognitively, the policy improved rural network and logistics infrastructure, highlighted the value of the internet in production and sales, shifted farmers' traditional conservative mindsets, and strengthened positive expectations regarding e-commerce for income growth. This stimulated their intrinsic motivation to actively participate in e-commerce and diversify livelihood channels, thereby solidifying the psychological and cognitive basis for resilience enhancement. Socially, leveraging demonstration county platforms, the policy connected with industry associations and e-commerce cooperatives to conduct training and resource matching, integrating farmers into organized systems. Utilizing kinship and geographic networks, it facilitated information sharing and peer demonstration effects, building a supportive social environment and consolidating external support for risk resistance. In terms of skills, the policy provided supporting practical e-commerce training, addressing farmers' deficiencies in digital skills. It enhanced their practical abilities in online operations, customer maintenance, and logistics coordination, overcoming psychological resistance related to e-commerce operations. This empowered farmers to optimize resource allocation and flexibly respond to market risks, providing solid skill support for enhancing livelihood resilience. Heterogeneity Analysis: From an economic regional perspective, the policy's effect was significant in central and western regions but insignificant in eastern regions. Regarding policy support background, the policy effect was stronger in poverty-stricken counties and non-old revolutionary base areas. Concerning county-level logistics infrastructure, areas with better logistics conditions exhibited a more pronounced policy effect. [Conclusions] The rural e-commerce policy significantly enhanced farmers' livelihood resilience, a conclusion that holds after multi-dimensional robustness checks. Mechanism analysis verified the crucial roles of cognitive transformation, organizational embedding, and skill improvement in the policy transmission process. Heterogeneity manifests as significant regional and conditional differences in the policy effect. In the future, policy design should shift from infrastructure investment towards systemic capacity building, continuously prioritizing resources towards central and western regions and poverty-stricken counties, while simultaneously improving the county-level circulation system.

[Objective] Leveraging digital technology to enhance grain production resilience is pivotal for ensuring food security and constructing a modernized agricultural support system. The purposes of this study are to: (1) Define the connotation of grain production resilience and construct an evaluation index system across three dimensions: risk resistance, regulatory adaptability, and transformative innovation; (2) Identify the impact and mechanisms of digital technology as a new production factor on grain production resilience; (3) Reveal how digital technology influences resilience through the reorganization of "People-Land-Service" factors; (4) Analyze the heterogeneity of these effects across diverse geographical environments and economic endowments. [Methods] Based on panel data from 30 Chinese provinces spanning 2011 to 2023, a rigorous empirical analysis was conducted. First, the entropy method was employed to measure the levels of digital technology and grain production resilience. Second, a dual machine learning model, utilizing support vector machine as base learners with 5-fold cross-validation and double residualization, was constructed for causal inference to address the "curse of dimensionality" and specification biases inherent in traditional linear models. To ensure robustness, an instrumental variable approach was applied, using the product of the number of fixed-line telephones in 1984 and the national internet users of the previous year. Third, a mediation effect model was introduced to test three pathways: non-farm labor transfer, large-scale land management, and agricultural productive services. [Results and Discussions] Mechanism analysis indicated: (1) In the "People" dimension, digital technology promoted non-farm labor transfer by reducing information search costs, thereby inducing capital reflux to alleviate financial constraints; (2) In the "Land" dimension, digital platforms lowered transaction costs for land transfer, facilitating contiguous large-scale farming and improving the marginal efficiency of digital equipment; (3) In the "Service" dimension, digital technology empowered agricultural service organizations, enhancing resilience through technology diffusion and professional intervention. Heterogeneity analysis showed that the empowerment effect was more pronounced in grain production-marketing balanced zones, high-relief terrains, and economically underdeveloped regions. This highlighted the "gap-filling" nature of digital technology, where precision tools like drones and remote sensing compensated for the limitations of traditional machinery in complex terrains. Notably, while digital technology significantly drove overall resilience, it primarily bolstered regulatory adaptability and transformative innovation while exerting a significant negative impact on initial risk resistance. [Conclusions] Digital technology is not a mere superposition of factors; rather, it catalyzes a fundamental transformation in the dynamics of grain production through the systemic restructuring of elements. To this end, differentiated strategies should be implemented: Major grain-producing areas should prioritize full-chain data integration, whereas production-marketing balanced zones and ecologically disadvantaged regions should focus on overcoming digital infrastructure bottlenecks. Furthermore, a horizontal "digital feedback" compensation mechanism should be innovated, encouraging major consumption areas to shift from "blood transfusion" (passive aid) to "blood-making" (capacity building) through technology licensing and targeted talent support. Finally, a new system of digitalized agricultural social services should be cultivated, leveraging the scale effects of digital platforms to effectively bridge the gap between smallholder farmers and modern agriculture.

[Objective] Enhancing agricultural economic resilience is critical for ensuring national food security, advancing agricultural modernization, and responding to increasingly complex external shocks. Against the backdrop of rapid digital economy development, digital technologies are progressively becoming a key driver of agricultural economic resilience by reshaping production patterns, optimizing resource allocation, and strengthening risk-response capacity. However, existing studies are predominantly grounded in linear analytical frameworks, which are insufficient to capture the complex mechanisms of multi-dimensional factor interactions, and they pay limited attention to spatiotemporal heterogeneity. In this context, a configurational perspective is adopted to systematically examine how agricultural economic resilience is enhanced through the synergistic interaction of multiple factors by digital technologies. The aim is to provide a more comprehensive theoretical basis for formulating differentiated digital agriculture development policies. [Methods] Based on the technology-organization-environment (TOE) framework, an analytical model was constructed for digital technology-driven agricultural economic resilience. Using provincial panel data from China spanning 2011 to 2023, a dynamic qualitative comparative analysis (QCA) approach was employed to identify multiple configurational pathways linking combinations of conditions to agricultural economic resilience. Specifically, necessity analysis was first conducted to examine whether any single antecedent condition was a necessary condition for the outcome. Second, configurational analysis was used to identify multiple equifinal pathways through which different combinations of conditions generate high agricultural economic resilience. Third, between-group and within-group analyses were further conducted to examine the temporal evolution and spatial heterogeneity of these configurational pathways. In addition, robustness tests were performed by adjusting the original consistency threshold, frequency threshold, and the threshold of proportional reduction in inconsistency to validate the stability of the identified configurations. [Results and Discussions] (1) A single digital technology factor was not a necessary condition for achieving high agricultural economic resilience. Instead, resilience improvement depended on the coordinated configuration of multiple dimensions, exhibiting a typical "multiple concurrent causality" characteristic. (2) Five effective configurational pathways were identified for enhancing agricultural economic resilience through digital technologies. These pathways were further summarized into three typologies: policy-market dual-driven type, production-chain enabling type, and synergistic driving type, reflecting the equifinality of different condition combinations. (3) The configurational pathways exhibited pronounced spatiotemporal heterogeneity. Temporally, the effects of certain pathways evolved dynamically with the deepening penetration of digital technologies. Spatially, due to differences in resource endowments, Eastern, Central, and Western regions, as well as different grain functional zones, corresponded to distinct dominant pathways. In terms of regional heterogeneity, production-chain enabling and synergistic driving pathways were mainly concentrated in Western and Eastern regions. In terms of grain functional zoning, the policy-market dual-driven pathway was primarily observed in major grain-producing and balanced production-marketing areas, while production-chain enabling and synergistic driving pathways were more prevalent in major marketing and balanced production-marketing regions. [Conclusions] The results demonstrate that there is no single optimal pathway for enhancing agricultural economic resilience; rather, it fundamentally depends on the effective synergistic configuration of multiple factors under specific contexts. As a core driving force, digital technology must be aligned with institutional environments, organizational capabilities, and resource endowments to fully realize its enabling effects. Meanwhile, regional heterogeneity further shapes the diversity of digital technology's impact pathways, implying that different regions should adopt development models tailored to their own conditions and continuously optimize them dynamically. From a configurational perspective, the results reveal the multi-path mechanisms through which digital technologies enhance agricultural economic resilience, enrich the existing theoretical literature, and provide important policy implications for implementing differentiated strategies, optimizing digital agriculture development pathways, and strengthening agricultural system resilience.

[Objective] Enhancing agricultural economic resilience is a critical strategic path for ensuring national food security and promoting the comprehensive implementation of rural revitalization. Against the backdrop of accelerated digital penetration in rural areas, existing research often focuses on macro-level digitalization, making it difficult to isolate the authentic contribution of digital inputs to agricultural systems. The driving effects and internal mechanisms of information and communication technology (ICT) input on agricultural economic resilience are explored in this research. Through clarifying its asymmetric impacts on resistance, recovery, and development capacities, a robust theoretical reference and empirical basis are offered for formulating differentiated digital agriculture policies that transition from traditional production modes to intelligent, resilient systems. [Methods] Based on Chinese provincial non-continuous panel data for 2012, 2015, 2017, 2018, and 2020, an advanced input-output (I-O) model framework was utilized. Leveraging the multi-regional input-output tables, the Leontief inverse matrix was employed to calculate the total consumption coefficient of the "Information Transmission, Software, and Information Technology Services" industry by the agricultural sector, which defined the total digital technology input value. Simultaneously, the entropy weight method was used to construct a comprehensive evaluation system for agricultural economic resilience. In terms of the econometric strategy, potential endogeneity was addressed by selecting the product of rural radio stations in 1988 and the previous year's Internet users as an instrumental variable (IV). The analysis was further supported by a 5% bilateral winsorization and a mediation effect model for rigorous empirical testing. [Results and Discussions] The empirical results demonstrated that digital technology input significantly enhanced overarching agricultural economic resilience. Benchmark regressions showed that the coefficient of ICT input was significantly positive at the 1% level, and the driving effect remained robust after correcting for endogeneity bias, which confirmed the core role of digital transformation in systemic risk management. Dimensional decomposition revealed a significant asymmetric characteristic: Digital technology strongly drives recovery capacity after exogenous shocks and developmental capacity during long-term evolution. However, its impact on the resistance dimension was relatively limited and exhibited a marginal negative effect. This reflected a potential technological dependence risk, where the system's increased sensitivity to power grids and network stability might weaken its original stress-resistance capacity during the onset of extreme risks. Furthermore, control variable analysis showed that per capita gross domestic product (GDP) and optimized planting structures promoted resilience, while the number of rural cooperatives exerted a negative influence, suggesting that some grassroots organizations suffered from insufficient digital adaptability. Mechanism analysis indicated that marketization, economic efficiency, and transport density were the primary transmission paths. Specifically, the marketization path contributed most significantly by reducing institutional transaction costs. Additionally, digital technology improved output efficiency through precision management and optimized transport logistics in synergy with physical infrastructure. Heterogeneity analysis showed that digital technology exhibited a clear "digital compensation" advantage in Western China, effectively offsetting natural resource endowment disadvantages. [Conclusions] This study confirms that digital input constitutes a new quality productive force that fundamentally strengthens the risk-resistance capacity of agricultural systems. The conclusions are summarized as follows: First, the empowerment of agricultural resilience by digital technology is characterized by a profound asymmetry. While it significantly improves the efficiency of systemic recovery and evolutionary development, it may simultaneously weaken original resistance due to intensified technological coupling and infrastructure dependence. Second, the reduction of institutional transaction costs through marketization is identified as the core mechanism for digital factors to exert their resilience-enhancing effects. The depth of the digital dividend is largely determined by the maturity of the market environment and its capacity for factor mobility. Third, the release of digital dividends in agriculture is heavily constrained by organizational adaptability. The lagging digital transformation and inherent structural rigidity of certain grassroots organizations have become the primary institutional bottlenecks restricting the conversion of digital technology inputs into practical systemic resilience. Ultimately, achieving a resilient agricultural economy requires a synergistic alignment between advanced digital production forces and modernized rural production relations.

[Objective] The agricultural product processing industry constitutes an important component in the economic system that plays a pivotal role in the construction of the entire agricultural industry chain and the realization of rural industrial revitalization. Despite China's agricultural product processing industry is experiencing the paradigm shift from a recovery-oriented development phase toward a high-quality development trajectory, there remains a gap between its current status and both the strategic development targets and the industrial benchmarks established by advanced economies. The purpose of the research is to: (1) Calculate the total factor productivity (TFP) within China's agricultural product processing industry to serve as a proxy variable for the high-quality development of the industry; (2) Identify the impact of the digital economy as an emerging economic paradigm on the total factor productivity of China's agricultural product processing industry and elucidate the mediating role of research and development investment in this relationship; (3)Analyze the heterogeneous impacts of the digital economy on the TFP of the agricultural product processing industry across varying governance environments, business operational conditions and enterprise management proficiency levels. [Methods] First, based on the panel data of agricultural product processing related enterprises listed on China's A-share market from 2012 to 2024, the TFP was measured by the Solow residual approach and the Levinsohn-Petrin (LP) method. Through the comprehensive construction of an indicator system and the utilization of provincial-level panel data, the developmental level of the digital economy was systematically measured. Second, a two-way fixed effects model was employed to identify the causal effect of the digital economy on TFP. In order to ensure the accuracy of data, robustness checks were conducted by replacing the baseline model with a Tobit model and by using alternative measures of the dependent variable. To address potential endogeneity, the one-period lagged value of digital economy development was used as an instrumental variable and the two-stage least squares (2SLS) method was adopted. In addition, a mediation model was introduced to test the channel effect of RD expenditure. Finally, the heterogeneity of the digital economy's impact on total factor productivity was analyzed from the perspectives of digital government, the business environment and enterprise management expense ratios. [Results and Discussions] The digital economy can effectively enhance the total factor productivity of the agricultural product processing industry. Mechanism analysis indicated that the enterprise RD investment played a partial mediating role in this relationship. These results remained robust after endogeneity treatment and robustness tests. Moreover, the impacts of the digital economy demonstrated significant heterogeneity when examined across different regional and enterprise dimensions. Specifically, with a relatively low level of digital government, the digital economy significantly inhibited the improvement of TFP; by contrast, with a more favorable business environment, the digital economy significantly promoted TFP growth. The conclusion demonstrated that only when digital government or business environment reaches advanced levels can it synergistically enhance the TFP of China's agricultural product processing industry with the digital economy. There was a significant negative relationship between management expense ratio and the impact of digital economy on TFP, which indicated enhancing management efficiency constituted a crucial pathway for improving firm-level total factor productivity. [Conclusions] Although the digital economy exerted substantial promotional impacts on the high-quality development of China's agricultural product processing industry, that still necessitated the attainment of specific thresholds across governmental governance environments, market operational conditions and enterprise management capabilities. Consequently, the efforts should be made to promote the deep integration between the digital economy and the agricultural product processing sector. On the one hand, the construction of digital government should be enhanced by extensively applying digital technologies to government service domains, implementing precision policy formulation to improve service accessibility and establishing stable, equitable, transparent and predictable policy and business environments for agricultural processing enterprises. On the other hand, agricultural processing enterprises should strategically leverage digital technologies to foster comprehensive innovation across technological, organizational and managerial dimensions, thereby realizing the optimization of their management tools and operational processes, the improvement of efficient resource allocation and utilization and the rational cost control. Ultimately, in the modern digital economy landscape, the overall operational efficiency and competitive advantage of the agricultural product processing sector can be enhanced.

[Objective] Enhancing the modernization level of China's agricultural industry chain and supply chain is a crucial strategy for enabling the high-quality development of the rural economy. In the context of digital transformation, understanding the role of digital logistics in this process is of paramount importance. The purpose of this research is to systematically investigate the impact, mechanisms, heterogeneity, and spatial spillover effects of digital logistics on the modernization of the agricultural industry chain. [Methods] Panel data from 280 prefecture-level cities in China spanning the period from 2011 to 2023 were used as the basis for this research. Firstly, the entropy method was employed to construct comprehensive measurement indices for digital logistics and the modernization level of the agricultural industry chain. To empirically examine the relationship between digital logistics and agricultural industry chain modernization, a panel fixed-effects model was utilized. Furthermore, a mediation effect model was constructed to explore the underlying mechanisms through which digital logistics exerts its influence. Finally, to account for spatial interdependence, a spatial Durbin model (SDM) was applied to analyze the spatial correlation characteristics and spillover effects of digital logistics on agricultural industry chain modernization. [Results and Discussions] The empirical analysis yielded several key findings: (1) Baseline effect: Digital logistics significantly contributed to advancing the modernization of the agricultural industry chain. This positive effect remained robust and valid after a series of rigorous robustness checks, including substituting variables, adjusting sample periods, and excluding particular cities, as well as after addressing potential endogeneity concerns through instrumental variable methods. (2) Mechanism analysis: The mediation effect analysis revealed that digital logistics promoted agricultural industry chain modernization through three primary channels of improving resource allocation efficiency, enhancing market accessibility, and boosting entrepreneurial activity. Specifically, digital logistics reduced information asymmetry and transaction costs, leading to a more efficient distribution of production factors; extended market reach for agricultural products; and lowered entry barriers, stimulating local entrepreneurship. (3) Heterogeneity analysis: The impact of digital logistics was not uniform across different regions. The heterogeneity test results demonstrated that the promoting effect of digital logistics on agricultural industry chain modernization was significantly stronger in the southern region compared to the northern region. Additionally, the effect was more pronounced in non-major grain-producing areas than in major grain-producing areas. Regarding the level of agricultural agglomeration, the positive impact of digital logistics was greater in regions with a high degree of agricultural industry agglomeration, suggesting that the benefits of digital logistics were amplified in areas with established industrial clusters. (4) Spatial spillover effects: The spatial econometric analysis revealed a non-linear spatial spillover pattern. The results indicated that the spatial spillover effect of digital logistics on the modernization of the agricultural industry chain in neighboring regions shifted from a negative effect to a positive effect as the spatial distance increased. This transition was observed approximately within the range of 0 to 600 km. Notably, the largest positive spatial spillover effect was detected at a distance of around 800 km, implying that the beneficial impacts of digital logistics in one city could significantly enhance the agricultural industry chain modernization of other cities located approximately 800 km away. [Conclusions] Based on these findings, several policy recommendations are proposed. First, it is essential to strengthen the empowerment of digital and intelligent technologies within the agricultural logistics system. Second, efforts should be made to dismantle "institutional barriers" and eliminate external obstacles that hinder the improvement of resource allocation efficiency. Third, policies should be tailored to local conditions, leveraging the role of digital logistics in optimizing the modernization of the agricultural industry chain according to the specific characteristics of different production areas.

[Objective] In the context of climate change, increased production risks, the digital reconfiguration of the allocation of agricultural factors, and the current challenges of food security, green development, and rural revitalisation, traditional agricultural economics research based on small-sample survey, the aggregated statistics, and the linear causal inference has shown decreasing ability to describe nonlinear relationships, spatial-temporal heterogeneity and dynamic interactions in agricultural production and rural society. This research aims to explore the paradigm shift of agricultural economics research catalyzed by big data technologies, explain the implications of big data for the discipline's evidence foundation, analytical thinking and organizational forms, and to draw out the key analytical, data-governance, and ethical problems that come with this shift. It further intends to provide theoretical references for constructing an independent agricultural economic knowledge system with Chinese characteristics amid the ongoing digital transformation of agriculture. [Methods] Drawing on Kuhn's theory of scientific paradigms, the agricultural economics research paradigm is a historically situated ensemble of common commitments, data conventions, and analytical practices and disciplinary norms that guided research. The historical trajectory of agricultural economics in China was traced, and the big-data-driven transformation was examined across seven dimensions: data source, research carrier, research object, research approach, research method, evaluation standard, and research organization. The analysis focused on the way the construction of digital infrastructure and the rise of intelligent analytics transformed agricultural knowledge production. Salient risks attending the deployment of big data were also identified, including biases in data, loss of capacity for causal identification, algorithmic opacity, and new challenges in the field of research ethics and governance. [Results and Discussions] Chinese agricultural economics has experienced four eras of evolution: the early theoretical importation and localization period, the rise of quantitative and econometric analysis period, computationally-assisted inquiry period, and data-intensive scientific inquiry period. The evidentiary base of the field has been transformed, as a massive volume of multi-source data, derived from different technologies, has emerged. Research that used to be limited to small scale, low frequency, structured datasets is now increasingly based on large-scale streams of high-frequency, multi-modal, near real-time data, greatly extending the range and granularity of empirical research. In addition to household surveys and field observations, a hybrid virtual-physical platform, such as digital twin systems, platform-based monitoring and integrated data observatories, was now introduced. The focus expanded from a relatively homogeneous group of farm households to a much more diverse group of small farmers, agribusinesses, actors in the digital platform, rural service providers, and policy actors. Research questions, which were mainly related to static production decisions, were increasingly focusing on bounded rationality, long-term dynamics of behaviour and the ecological-economic-social interactions of rural transformation. Research strategies became more future-oriented, combining forecasting, the modeling of scenarios and cycles of theory revisions. At the method level, machine learning, double machine learning, causal forests, and other tools started to complement standard econometrics, allowing to expand the toolkit to predictive modeling, heterogeneity analysis and more sophisticated causal inference. Evaluative standards were revised also: data quality, predictive fidelity, out-of-sample validity and method-problem fit grew in importance relative to theoretical parsimony and statistical significance alone. Research shifted from isolated, discipline-based research teams to open, collaborative research ecosystems, based on interdisciplinary collaborations and data-sharing platforms. This change was accompanied by four challenges. First, issues of representativeness of data at the data level arose as digital cleavages and a false sense of comprehensiveness often underrepresented remote areas, elderly, and digitally ignored smallholders. Second, at the methodological level, the correlation-causation dichotomy was more prominent since machine learning has been more successful at prediction than in identifying generative mechanisms. Third, algorithmic opacity led to issues of transparency, accountability and fairness in various contexts, including in agricultural subsidy allocation, credit scoring and risk assessment. Fourth, there were increasing risks of privacy infringement and data insecurity, as agricultural big data can also include sensitive personal, operational and strategic data. [Conclusions] Big data technology is driving a transition of agricultural economics from a purely model-centric paradigm towards a data-model co-driven "fourth data-intensive scientific paradigm". It reframes the questions addressed by the discipline, the assumptions it makes, the analytical frameworks it utilizes and the organization of research. Future efforts should focus on four aspects: establishing national unified agricultural big data infrastructure, the integration of data resources, analytical models and algorithmic tools into a shared infrastructure, training and research activities to be undertaken in an interdisciplinary fashion, and constructing governance mechanisms and ethics for agricultural big data.

[Objective] Smart agriculture serves not only as a critical measure for driving agricultural and rural modernization, but also as a core component of new quality productive forces in agriculture. In recent years, China's smart agriculture has achieved remarkable development. However, it still faces practical challenges in policy implementation, technological application, and industrial coordination, which restricts the high-quality development of agriculture. Against this background, quantitative analysis of the central-level smart agriculture policy system was conducted from the crucial link of policy design. Grounded in practical dilemmas of smart agriculture development and top-level guiding requirements, policy logic and inherent deficiencies were clarified via scientific analytical approaches.The aim of this research is to identify the direction of policy optimization, so as to provide practical references for promoting the quality and efficiency improvement of smart agriculture and advancing the construction of a strong agricultural country. [Methods] From the perspective of policy tools, first, a three-dimensional analytical framework of "tools-topics-objectives" was constructed, which served as the core analytical logic of the overall research. A total of 240 central-level policy documents concerning smart agriculture issued from 2013 to 2025 were selected as the research sample. Systematic coding and quantitative analysis of all collected policy texts were implemented through content analysis with the assistance of NVivo14 qualitative analysis software. To further explore the latent thematic structures of China's smart agriculture policy texts, accurately identify the focal directions of policy content and their long-term evolutionary characteristics, the Latent Dirichlet Allocation (LDA) topic model was further adopted to realize automatic text topic analysis. Through the above integrated research methods and analytical pathways, core characteristics of China's smart agriculture policies were systematically summarized, and targeted optimization strategies were put forward. [Results and Discussions] First, the analysis identified four distinct evolutionary stages: the Policy Exploration Period (2013－2015), the Pilot Demonstration Period (2016－2018), the Strategic Promotion Period (2019－2023), and the Comprehensive Deepening Period (2024 to present). This progression reflected a shift from conceptual introduction and top-level design to concrete technology application, strategic scaling, and finally, systematic institutionalization. Second, in terms of policy instrument deployment, it was found that supply-side, environmental, and demand-side tools were used in a relatively balanced overall proportion. However, significant structural imbalances existed within each category. Supply-side tools were heavily skewed towards infrastructure construction and scientific research support, with relatively less emphasis on direct financial input and talent cultivation. Environmental tools were dominated by publicity and guidance, while financial support mechanisms and standard-setting were underutilized. On the demand side, policy relied heavily on pilot demonstrations, with weaker emphasis on fostering cooperation, industry-research integration, and broader social participation. Thirdly, the evolution of policy objectives exhibited an obvious phased and progressive feature, whereas its internal structure still needed optimization. On the whole, national smart agricultural policies covered five core fields, with the digital and intelligent transformation of production and operation as the primary objective. Technological innovation and data services acted as crucial supporting pillars. By contrast, two guarantee-oriented objectives, namely talent team development and institutional standard improvement, accounted for merely 10.91% and 10.13%, respectively, resulting in a prominent imbalance in the objective system. Furthermore, policy development could be divided into four sequential stages: Initial conceptual guidance and foundational planning, pilot-oriented practical implementation, systematic policy construction, and in-depth quality and efficiency upgrading, forming a clear progressive trajectory. Though digital transformation had long served as the central focus across all stages, the development of talents and institutional mechanisms had witnessed gradual yet relatively slow progress, constantly lagging behind the advancement of core policy objectives. Fourthly, the LDA topic model extracted four core policy themes: Digital transformation and intelligent upgrading, industrial integration and business model innovation, talent cultivation and entrepreneurial support, and technical equipment and intelligent application. A striking finding was the extreme concentration of policy attention, with the digital transformation and intelligent upgrading theme accounting for 57.92% of the thematic focus, significantly overshadowing the other three themes combined, the matching degree between policy themes and policy instruments needs to be improved. [Conclusions] While China has developed a comprehensive policy framework for smart agriculture, its effectiveness is hampered by structural imbalances in instrument use, an over-concentration on a single policy theme, and an inconsistent coordination of policy objectives. To address these challenges and foster a more robust and sustainable development path, three key optimization strategies are proposed. First, promoting the balanced deployment of policy tools by following a tripartite approach: upgrading supply-side tools, empowering environmental-side tools, and driving demand-side tools, so as to enhance synergy and complementarity among different tool types. Second, optimizing the allocation of attention to policy themes and enhance the alignment between policy instruments and policy themes, avoiding over-reliance on individual fields and under-support for key sectors. Third, advancing the coordinated optimization of policy goals, and improving the systematic mechanism led by standards, guaranteed by institutions, and supported by talent teams. Implementing these strategies will be crucial for transforming China's smart agriculture policy system from one focused on quantity and foundational building to one capable of driving high-quality, balanced, and innovative development in the years to come.

[Objective] Agricultural operators are the initial origin and key contributors to smart agriculture data, and the protection of their data rights and interests urgently needs to be addressed. China's smart agriculture is currently developing rapidly, and the digitalization level of agricultural production continues to rise. However, agricultural operators' data rights and interests have not received sufficient attention. What rights and interests they should enjoy and how these can be realized have become pressing practical issues. The aim of this research is to reveal the nature and connotation of agricultural operators' data rights and interests, analyze the dilemmas in rights protection, and propose institutional responses, and form a virtuous cycle of "data generation-value creation-rights feedback", thereby promoting the healthy and sustainable development of smart agriculture. [Methods] Literature review, normative analysis, case analysis and comparative analysis were adopted. The literature review was conducted to examine existing research progress and to identify the research questions. Through normative analysis and systematic interpretation of the data rights rules in China's existing legal norms and relevant policy documents, the nature of rights and interests of agricultural operators' non-personal agricultural data was defined. Representative institutional norms of foreign agricultural data governance were compared to distill referable experience for the protection of agricultural data rights and interests. Typical cases were used to concretely illustrate the practical dilemmas in protecting agricultural operators' data rights and interests in the development of smart agriculture. [Results and Discussions] The results demonstrated that agricultural operators' data rights and interests constituted a legal interest arising from their contribution as a data source, protected by civil law but not elevated to a right. The subjects of rights and interests included rural contract-management households, family farms, specialized farmer cooperatives, agricultural enterprises, etc. The object was non-personal agricultural data within agricultural data. Based on the theories of fruits of rights and "digital labor", agricultural operators held property-type rights and interests in their non-personal data. It further revealed that in practice, the protection of agricultural operators' data rights and interests faced fourfold dilemmas. First, the lack of a basis for claims: Existing laws do not provide protection for non-personal agricultural data. Second, the imbalanced autonomy of will: Contract terms were unilaterally drafted by data processors, leaving agricultural operators with limited bargaining power and substantive choice. Third, the technological control or constraints: Data processors locked in data through technical means, while the absence of technical standards for agricultural data interoperability made data portability unattainable. Fourth, the difficulties in rights and interests relief: The highly concealed technical nature of data processing made it hard for agricultural operators to trace data flows and to prove damages and causation, coupled with the generally low digital literacy of agricultural operators and their fragmented strength, which made it challenging for them to enforce their rights. [Conclusions] A multi-layered institutional framework encompassing "rights confirmation - contractual checks and balances - data governance - mechanism innovation" should be established to safeguard agricultural operators' data rights and interests. At the legal level, the content of agricultural operators' data property interests should be clearly defined, and non-exclusive data interests including the right to know, to access and control, to portability, and to benefit should be constructed based on the bundle-of-rights theory. At the contractual level, relevant authorities should formulate model texts for standard agricultural data contracts to rectify the substantive inequality between agricultural operators and data controllers. In terms of data governance, technical rules and legal norms should be integrated by unifying data technical standards, refining data classification and grading, and establishing compliance certification systems. In the dimension of mechanism innovation, considering the particularities of agricultural operators' data, the introduction of a collective governance model through data trusts should be explored, relying on specialized operation and collective management to ensure the effective realization of data rights and interests.