[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.

[Significance] Digital agriculture is unequivocally the core driving force for modern agricultural transformation, fundamentally aiming to achieve full-process digital mapping and intelligent management of production through the deep integration of advanced information technologies such as the Internet of Things, big data, artificial intelligence (AI), and remote sensing, with earth observation (EO) technology serving as the essential data engine providing indispensable spatial information support for this systemic shift. However, the current landscape of digital agriculture development remains unbalanced, exhibiting a tendency to be "heavy on transactions and light on production", where the core production links suffer from low digitalization penetration rates; furthermore, the profound knowledge embedded within the vast corpus of EO data has yet to be fully extracted and interpreted, leading to a situation where many established algorithms demonstrate insufficient robustness and universality when confronted with the complexity and diversity of global cropping systems, thereby limiting their practical efficacy. Crucially, an over-reliance on technology to optimize production efficiency alone, without ecological guidance, can induce secondary environmental risks, such as exacerbating regional groundwater depletion or contributing to a decline in biodiversity through agricultural landscape simplification, thus necessitating an approach that promotes the deep coupling of EO technology with agronomic principles and local ecological practices to construct a resilient smart agricultural system that achieves a holistic balance between productivity, resource efficiency, and ecological integrity. [Progress] The current research frontiers of EO-driven digital agriculture primarily converge on three critical domains: intelligent crop condition monitoring, digital twin farming systems, and the enhancement of agricultural system resilience. Intelligent monitoring utilizes the fusion of high-resolution remote sensing imagery and machine learning frameworks to enable large-scale, comprehensive crop mapping and the fine-grained identification of crop types at the field scale, with next-generation yield prediction models integrating advanced deep learning techniques to significantly improve accuracy, while remote sensing is also effectively employed for agricultural disaster monitoring. The digital twin farming system represents an advanced stage of precision agriculture, centered on digitally modeling all agricultural production elements to construct a highly consistent virtual replica of the physical environment, operating through a real-time closed-loop mechanism of perception, simulation and analysis, and decision-making support to guide optimal interventions; successful applications include intelligent water resource scheduling in Chinese irrigation districts and the use of AI vision algorithms to manage complex biological processes like crab farming, although the field must overcome the issue of "pseudo-twins" that focuses on mere visualization rather than driving concrete operational decisions. The focus on agricultural system resilience is supported by digital agriculture providing crucial spatial data on global crop yields, cultivated land distribution, and practices like terracing. To illustrate the practical efficacy of these technologies, this paper analyzes two representative application cases. First, the CropWatch system represents a paradigm shift in agricultural monitoring by constructing a "Cloud-Edge" collaborative ecosystem. It integrates machine learning with a "Pre-training, Prompting, and Fine-tuning" large language model (LLM) framework to automate remote sensing-based crop monitoring, report generation and enhance decision-support intelligence. Through open application programming interfaces (APIs) and multi-scale capabilities, CropWatch provides cross-scale information and decision support from macro-level policy support to micro-level farm management, serving as a global public good that bridges the digital divide in developing nations. Second, in the domain of agricultural water management, the ETWatch technical system demonstrates a robust solution for the precise governance of water resources. By achieving high-resolution evapotranspiration (ET) monitoring from basin to field scales, it enables the accurate assessment of water productivity and the optimization of irrigation schedules. Crucially, this technology is successfully embedded into institutional mechanisms, such as water rights allocation and tiered pricing based on actual consumption, thereby realizing a transformation from empirical water use to data-driven, precise regulation. [Conclusions and Prospects] In sum, digital (smart) agriculture is rapidly transcending its role as a mere extension of agricultural informatization to become the "new-quality productivity" driving high-quality agricultural development, achieving this by fundamentally restructuring production factors, enhancing resource efficiency, strengthening risk response capabilities, and promoting value chain upgrading, thereby offering critical momentum for constructing a more efficient, greener, and sustainable modern agricultural system. Given China's pronounced global advantages in the digital economy, information technology, remote sensing, and intelligent equipment, the nation is well-positioned to integrate these strengths to construct comprehensive, full-chain smart agricultural solutions whose mature systemic models and business paradigms can ultimately form a "China Card" in the global agricultural revolution, contributing Chinese wisdom and solutions towards the realization of global food security and the zero-hunger goal.

[Significance] Under climate change, the frequency and intensity of extreme weather events have increased markedly, posing persistent threats to global food security. Agricultural meteorological disasters, including droughts, floods, heat stress, frost damage, and mechanically induced events such as lodging and hail, are increasingly characterized by rapid onset, strong spatial heterogeneity, and compound interactions. Conventional management strategies relying mainly on post-event assessment are insufficient for timely warning and precision intervention. The development of high spatiotemporal resolution remote sensing and integrated observation systems combining satellite, unmanned aerial vehicle (UAV), and ground-based sensing has substantially advanced agricultural disaster monitoring. These technologies enable field-scale characterization of spatial variability and detection of short-duration disaster processes at hourly to daily timescales. This review synthesizes recent progress in sky-air-ground integrated remote sensing for agricultural meteorological disaster management and establishes a unified framework linking monitoring, early warning, and decision-making, with emphasis on hydrological stress, thermal stress, and structural damage. [Progress] At the observation level, a multi-tier sensing architecture has emerged. Satellite remote sensing provides broad coverage and regular revisit cycles, forming the backbone of regional monitoring. Optical sensors support retrieval of crop structural and biochemical parameters, thermal infrared data enable canopy temperature and evapotranspiration estimation, and synthetic aperture radar (SAR) offers all-weather capability for soil moisture and flood detection. Solar-induced chlorophyll fluorescence (SIF) provides direct information on crop photosynthetic function and enables early identification of physiological stress. UAV platforms complement satellites through flexible deployment and centimeter-scale resolution, allowing detailed mapping of canopy temperature and three-dimensional crop structure using multispectral, thermal, and light detection and ranging (LiDAR) sensors. Ground-based meteorological stations and sensor networks provide continuous measurements for calibration and validation, although scaling point observations to spatially continuous products remains challenging. Consequently, multi-sensor integration is evolving from data stacking toward physically complementary constraint frameworks. Methodologically, two dominant approaches of physically based inversion and data-driven recognition are used. Radiative transfer models, surface energy balance methods, and SAR scattering models offer strong physical interpretability but depend on prior information and data quality. Machine learning and deep learning methods effectively capture nonlinear relationships and complex spatial patterns for disaster identification, yet remain limited by interpretability and cross-regional generalization. At the early-warning stage, crop growth models, hydrological models, and spatiotemporal prediction networks are applied to simulate disaster evolution. Hybrid models embedding physical constraints into data-driven frameworks have become a key research direction to enhance predictive robustness. Decision-support systems have expanded from threshold-based rule engines toward optimization algorithms and multi-objective frameworks, enabling warning information to be translated into actionable irrigation scheduling, protective measures, and emergency responses. Regarding specific hazards, drought monitoring has shifted from vegetation indices toward coupling root-zone soil moisture with crop physiological responses, with SIF-based indicators showing strong potential for early stress detection. Flood studies rely primarily on SAR-based inundation mapping and extend toward quantitative damage assessment. Heat and frost stress research emphasizes growth-stage-dependent dynamic thresholds. Lodging monitoring integrates structural parameters derived from optical, LiDAR, and SAR data, while hail-related studies focus on rapid post-event damage mapping. Compound and cascading disasters have become an important research frontier. [Conclusions and Prospects] High spatiotemporal resolution remote sensing has greatly enhanced the observability and early-warning potential of agricultural meteorological disasters. Nevertheless, key challenges remain, including heterogeneous data integration, scale inconsistency, uncertainty propagation, and insufficient coupling among monitoring, warning, and decision-making components. Future progress requires a systems-engineering perspective. Physically guided machine learning can bridge mechanistic understanding and data adaptability, while agricultural disaster digital twins provide a framework for dynamic interaction among observation, simulation, and decision optimization. In parallel, multi-factor time-series risk modeling and multi-agent learning are needed to better represent compound disaster processes and support intelligent, adaptive, and precision-oriented agricultural disaster management systems.