[Objective] Accurate temporal localization of mounting behavior in dairy goats is important for intelligent reproductive management, as event frequency, onset time, and duration provide useful evidence for heat monitoring and mating decisions. Unlike simple behavior recognition, temporal localization in untrimmed videos enables fine-grained, time-resolved records for practical farm use. However, real-world mounting behavior is usually brief and sporadic, with few informative frames in long video streams. Moreover, weak discrimination from similar non-target interactions, together with occlusion, viewpoint variation, and background motion, often degrades boundary-aware representation learning and leads to unstable start-end localization. To enhance localization accuracy and stability while maintaining practical efficiency for deployment, an improved AdaTAD-based end-to-end temporal action localization approach was proposed for mounting behavior in dairy goats. [Methods] The proposed approach adopted AdaTAD as the baseline end-to-end temporal action localization framework and introduced two complementary improvements, explicit key-frame guidance and multi-scale motion modelling, while retaining the original detection head and post-processing pipeline for generating temporal action instances. First, visual prompt tuning (VPT) was incorporated to provide task-conditioned guidance to backbone feature extraction in a parameter-efficient manner. Specifically, a small number of learnable prompt tokens were inserted into the Transformer backbone with backbone parameters frozen. Through multi-head attention interactions between prompt tokens and patch tokens, the prompts steered attention towards mounting-relevant temporal regions, strengthened feature responses at critical frames and in boundary neighbourhoods, and improved the separability between brief target segments and abundant background frames. Second, a multi-scale motion adapter (MSMA) was introduced to model motion patterns at different temporal scales and improve robustness to diverse scene dynamics. MSMA employed parallel multi-scale temporal depthwise separable convolution branches to capture short-, mid-, and longer-range temporal variations, enhancing representations of subtle short-duration micro-actions as well as relatively complete action processes. Residual connections and nonlinear mappings further stabilised feature injection and gradient propagation, enabling multi-scale dynamics to be integrated into backbone features with limited additional optimisation burden. Overall, VPT focused on boundary-relevant attention guidance, whereas MSMA emphasised multi-scale temporal dynamics modelling; Together, they formed a complementary design within the end-to-end localization pipeline. [Results and Discussions] Comparative experiments showed that the proposed method achieves an average mAP (mean Average Precision@[0.3:0.1:0.7]) of 81.72%, improving upon the baseline AdaTAD by 5.00 percentage points, indicating that incorporating VPT and MSMA enhanced overall localization performance. At a temporal Intersection over Union (tIoU) threshold of 0.7, the proposed method attained 68.85%, exceeding AdaTAD by 4.06 percentage points, demonstrating that the performance gain was preserved under stricter temporal boundary-consistency criteria. Further comparisons with representative approaches, including TadTR, VSGN, AFSD, ActionFormer, TriDet, DyFADet, and Re2TAL, showed average mAP improvements of 38.82, 33.83, 25.29, 4.09, 2.83, 1.20, and 6.06 percentage points, respectively, demonstrating stronger overall competitiveness. In terms of efficiency, the model ran at 65.78 f/s with 27.941 million trainable parameters, indicating that the accuracy gains were achieved while maintaining a relatively low parameter overhead and practical runtime efficiency. Overall, task-guided prompting and multi-scale temporal modelling improved key temporal feature representations with limited parameter increments, thereby benefiting localization of short, sporadic behaviors. [Conclusions] By combining VPT for boundary-relevant attention guidance with an MSMA for multi-scale temporal dynamics modelling, the proposed AdaTAD-based end-to-end temporal action localization method improves localization accuracy and maintains stable advantages under stricter boundary-consistency requirements, while preserving practical inference efficiency. The method could provide temporal information for reproductive behavior monitoring and decision support, and offer a feasible basis for building individual-level, time-resolved management systems in real farming environments.

[Objective] Carbon storage is a key indicator for measuring ecosystem functionality, with significant differences in carbon sequestration capacity among various land use types. Changes in land use directly lead to variations in terrestrial ecosystem carbon storage. Therefore, an in-depth analysis and prediction of the spatiotemporal distribution patterns of land use change and carbon storage can provide a scientific basis for achieving carbon sequestration targets and optimizing land use structures. [Methods] Taking Gaotang county, a typical plain agricultural county in Shandong province as the study area, based on land use types extracted from remote sensing data from 2009 to 2023, the spatiotemporal evolution patterns of land use were first analyzed. The InVEST model was then employed to estimate carbon storage and identify the spatial distribution patterns of carbon storage across different land categories. Driving factors were selected from the perspectives of natural conditions, socio-economic development, and locational conditions. Five scenarios: natural development, cultivated land protection, urban development, ecological protection, and sustainable development, were established. A coupled PLUS-InVEST framework was constructed. First, the PLUS model was used to simulate land use patterns under five scenarios for 2035. Then, the simulated land use maps were input into the InVEST model to estimate carbon storage and to compare changes across different scenarios. This coupling enables an integrated analysis linking policy scenarios, spatial land use patterns, and carbon storage responses. [Results and Discussions] From 2009 to 2023, significant changes occurred in land use types, with the most notable transformation being the conversion from cultivated land to forest land. Overall, cultivated land area fluctuated and decreased by 3 184.10 hm²; forest land in the southwestern region increased by 1 988.74 hm²; and construction land in the county center decreased by 109.89 hm². Total carbon storage increased by 1.14×10⁵ t. Carbon storage was significantly correlated with the spatial distribution of various land types, exhibiting a pattern of higher values in the southwest and lower values in the northeast. Carbon storage in the county center was relatively low, while the southwestern region, rich in forest land resources, had the highest carbon storage. Under the natural development scenario, all land use types except cultivated land showed a decrease in area. Under the cultivated land protection scenario, cultivated land area reached 69 116.78 hm², an increase of 8.23% compared to 2023, while forest land experienced the largest decrease. Under the urban development scenario, both cultivated land and construction land increased, whereas all other land types decreased. Under the ecological protection scenario, cultivated land, forest land, and grassland increased, while all other land types decreased. Under the sustainable development scenario, cultivated land area was slightly smaller than that under the cultivated land protection scenario, while forest land and grassland areas were similar to those under the ecological protection scenario. The spatial distribution characteristics of carbon storage were similar across all scenarios, consistently showing a pattern of lower values in the central area and higher values in the surrounding areas. Under the urban development scenario, total carbon storage was the lowest among all scenarios, decreasing by 3.01×10⁵ t compared with the 2023 level. [Conclusions] This research reveals that construction land expansion is the main cause of carbon storage loss, while ecological restoration measures can effectively increase carbon storage. The spatial distribution of different land categories remains relatively stable across scenarios, with carbon storage exhibiting a pattern of lower values in the central urban area and higher values on the periphery. Strategies pursuing only economic development or focusing solely on cultivated land protection have inherent limitations. The sustainable development scenario can better balance cultivated land protection and ecological conservation, dynamically adjust the relationship between the two, and achieve their long-term coordinated development. This research could provide a reference for achieving carbon peak, carbon neutrality, and sustainable development goals in plain agricultural regions.

[Objective] Maize production in Yunnan‑representative mountainous regions is strongly shaped by rugged terrain, fragmented cropland, and steep vertical climatic gradients, which drive pronounced spatial heterogeneity in soil nutrients and yield. Unlike homogeneous plains, yield variation here stems from the combined effects of soil fertility, climate, and topography rather than any single nutrient factor, making traditional statistics or isolated soil indicators inadequate. To this end, an interpretable machine learning framework integrating soil nutrients and spatial information was developed to enhance maize yield prediction and identify the dominant drivers of yield disparities across sub‑regions of such landscapes. [Methods] Soil testing and formula fertilization data from Yunnan province during 2005—2020 were combined with 5 km gridded maize yield data. Four soil nutrient indicators were selected, including soil organic matter, total nitrogen, available phosphorus, and available potassium. Longitude and latitude were also used as input variables. In this study, these two variables were not treated simply as location labels. They were included because spatial position can partly reflect regional environmental gradients that are difficult to describe using soil nutrient data alone, such as differences in elevation, heat and moisture conditions, terrain fragmentation, and cropping background. Based on these variables, particle swarm optimization was used to tune the main hyperparameters of the XGBoost model, forming a PSO-XGBoost yield prediction model. The optimized model was compared with LightGBM, CatBoost, random forest, support vector regression, and the original XGBoost model. Considering that nearby samples may share similar environmental conditions, random partitioning alone may overestimate model performance. Therefore, spatial block cross-validation was further used to examine the model's ability to predict yield under spatial separation. In addition, longitude and latitude were removed in an ablation experiment to test the contribution of spatial background information. Finally, SHAP was used to interpret the trained model, quantify the contribution of each variable, analyze the response of yield prediction to soil nutrients and spatial factors, and identify the dominant limiting factors in different maize-growing areas. [Results and Discussions] PSO-XGBoost showed the best performance among the tested models. Under random data partitioning, the model achieved an R² of 0.91, indicating that it could effectively capture the relationship among soil nutrients, spatial background, and maize yield. Compared with LightGBM, CatBoost, and the unoptimized XGBoost model, PSO-XGBoost gave more accurate and stable predictions. This result suggested that particle swarm optimization improved the parameter configuration of XGBoost and made it more suitable for yield prediction in complex mountainous regions. Under spatial block cross-validation, the R² decreased to 0.847. Although this value was lower than that obtained from random partitioning, it still showed good predictive performance under a stricter validation strategy. This also indicated that the model did not rely only on the similarity between neighboring samples, but retained a certain ability to predict yield in spatially separated areas. The ablation experiment further confirmed the importance of spatial information. After longitude and latitude were removed, the model R² dropped sharply to 0.698. This decline showed that maize yield patterns in Yunnan cannot be well explained by soil nutrient indicators alone. Spatial variables probably contain information related to elevation-induced climatic differences, regional hydrothermal conditions, terrain fragmentation, and differences in the cropping environment. The SHAP results led to a similar conclusion. Longitude and latitude had relatively large contributions, suggesting that regional environmental gradients played an important role in yield formation. Among the soil nutrient variables, soil organic matter, total nitrogen, available phosphorus, and available potassium were all related to maize yield, but their roles differed across Yunnan. In some areas, yield was more closely associated with organic matter and nitrogen supply, while in others phosphorus or potassium appeared to be more limiting. This spatial difference suggested that the main constraints on maize production were not the same across the province. For this reason, a province-wide fertilization scheme would be difficult to apply effectively. Nutrient management in Yunnan should instead be adjusted according to local soil conditions and the environmental background of different mountainous regions. [Conclusions] Maize yield in Yunnan province is affected by both soil nutrient conditions and broader spatial environmental factors. Soil fertility is still an important basis for yield formation in mountainous farmland, but it does not explain the spatial yield differences on its own. By combining PSO-XGBoost with SHAP, this study provides a way to improve yield prediction while also tracing the main factors related to regional yield variation. The framework can be used to identify areas where nutrient constraints are more likely to occur, as well as areas where terrain, climate background, or other regional environmental factors may have a stronger influence. These results can support more targeted fertilization, cultivated land quality improvement, and sustainable maize production in the mountainous agricultural areas of Yunnan.

[Objective] As the core physiological process determining crop water demand, transpiration profoundly influences irrigation-district water regulation and allocation strategies. During the dry season, water stress is particularly acute in the Yunnan savanna region. For smart, sensor-driven irrigation management, reliable orchard-scale transpiration estimation is essential for real-time irrigation scheduling and water allocation. However, traditional mechanistic models face challenges such as difficulties in parameter acquisition, high sensitivity to specific parameters, and limited applicability in complex terrains. To address these issues in the development of smart irrigation districts, a high-precision citrus transpiration estimation model was constructed with low parameter dependence. [Methods] A citrus orchard with 10-year-old trees in the savanna region was selected as the study site. Three years of stem sap flow observations and synchronous environmental-factor data were collected. Sap flow was accumulated to the daily scale and used to estimate field-scale transpiration. A Random Forest–based wrapper feature selection method was applied to identify an optimal subset of key predictors from 16 original environmental parameters and the day of year (DOY). Specifically, the wrapper method iteratively removed the variable with the lowest feature importance and rebuilt the model, continuing until only one variable remained. During this process, model performance under different numbers of variables was recorded for each iteration, and the best feature subset was determined based on performance. In addition, a heatmap was used to quantify linear correlations among variables to better understand redundant parameters removed by the model. After identifying the optimal feature combination, the built-in feature importance method of Random Forest was used for importance evaluation. Based on these parameters, extreme gradient boosting (XGB), support vector regression (SVR), and random forest (RF) models were established. Particle swarm optimization (PSO), bayesian optimization (BO), and the asynchronous successive halving algorithm (ASHA) were introduced for hyperparameter tuning, resulting in 12 simulation models in total. The dataset was split into 80% for training and 20% for validation. During training, 10-fold cross-validation was adopted, and mean absolute error (MAE) was used as the fitness function. Model performance was comprehensively evaluated using the coefficient of determination (R²), root mean square error (RMSE), MAE, and uncertainty metrics. [Results and Discussions] The RF wrapper feature selection reduced the 16 meteorological input parameters to four: actual vapor pressure (e_a), soil water content (VWC), wind speed at 2 m (u₂), and mean air temperature (T_a). In addition, the DOY was readily obtainable and was identified as the most critical feature, effectively characterizing the unique seasonal climatic variations of the Savanna Valley and citrus phenological characteristics; meanwhile, e_a, VWC, T_a, and u₂ mainly characterize short-term atmospheric evaporative demand and soil moisture constraints. While substantially reducing data requirements, the proposed method still achieved high prediction accuracy. Among all model combinations, XGB-ASHA performed best, reaching R²=0.91 and RMSE=0.19 mm/d for daily-scale transpiration prediction. Hyperparameter optimization improved model robustness, further reducing RMSE and MAE compared with non-optimized baselines. Compared with commonly used mechanistic models in the region, the proposed machine-learning approach does not require complex parameter calibration, is less sensitive to input errors, and shows better application feasibility. Uncertainty evaluation further indicates that this framework can provide not only accurate point predictions but also reliable confidence information for operational decision-making. [Conclusions] The proposed model requires monitoring only four environmental parameters to achieve high-accuracy, operational daily-scale transpiration estimation for citrus orchards in the savanna region. The optimal configuration, XGB-ASHA, is suitable for embedding into smart irrigation systems, supporting water-saving irrigation scheduling and irrigation-district-scale water management. Future work will integrate multi-source data and explore more advanced learning models to further improve transferability and generalization across orchards and years.

[Objective] Detecting cotton leaf diseases in natural field environments is challenging due to diverse image interferences, variable leaf‑spot sizes, and the demand for fast computation on mobile phones and other small‑scale devices. Nevertheless, existing lightweight models cannot well balance detection accuracy and computational efficiency, especially for detecting small lesions and suppressing noise surrounding leaves. To address these challenges, MHSF‑DETR (Multi‑Scale Heterogeneous Synergistic Feature DETR), an improved detection model based on the RT‑DETR framework, is proposed. It aims to achieve high‑precision, low‑power diagnosis in complex agricultural scenarios. [Methods] The primary innovation of this study consisted of the complete reconfiguration of the feature extraction and fusion architectures. Firstly, a hierarchical context-selective perception network (HCSP-Net) was constructed as the backbone to replace conventional architectures. This backbone employed a differentiated processing strategy tailored to the depth of the feature maps: In the early parts of the process where the features were simple, it used something called micro-macro spatial context attention (M²-SCA). This module used a channel semantic filter then a dual stream spatial perception structure to actively capture high frequency textures of micro-lesions and preserve macro semantics so that fine details were not lost when downsampled. At the deep feature stage, a competitive selection fusion (CSF) module was added. Unlike the traditional static summing approach, CSF created a dynamic competition arbitration system that flexibly balances local importance versus overall coherence via soft competition gates, making the semantics sharper and filtering away irrelevant background noise. Secondly, to tackle the spatial and semantic misalignment that was commonly seen in cross-level feature fusions, a learnable weighted context fusion (LWC-Fusion) module was created inside the neck network. This module used global amplitude dynamic weighting to learn autonomously the best blending ratios, so that deep semantic features were aligned precisely with shallow geometry. Moreover, to solve the problem of artifacts appearing at irregular leaf boundaries caused by traditional zero-padding convolutions, an edge-aware reconstruction mechanism (EARM) was proposed. By using edge-refined convolution (ER-Conv) and the edge-refined convolution C3 module (ER-ConvC3), which integrated reflection padding and partial convolution techniques, the model successfully curtailed invalid edge noise and diminished computational redundancy without sacrificing the geometric continuity of features. [Results and Discussions] Empirical benchmarks demonstrated that the proposed MHSF-DETR achieved a superior balance between detection performance and computational efficiency. Compared to the RT-DETR-R18 baseline, MHSF-DETR yielded a significant 3.2 percentage points increase in mean average precision (mAP), while simultaneously reducing parameters by 22.42% and GFLOPs by 13.29%. When benchmarked against mainstream detectors, MHSF-DETR consistently outperformed models such as YOLOv5m, YOLOv10m, and RT-DETR-R50. Although YOLOv8m maintained a marginal mAP50 lead in specific scenarios, its exorbitant computational overhead rendered it less practical for real-time deployment compared to MHSF-DETR. It successfully matched the lean efficiency of YOLOv10m but excelled in detection accuracy. Furthermore, extensive ablation studies confirmed that these performance gains stemmed from the structural synergy among the HCSP-Net backbone, the LWC-Fusion neck, and specialized reconstruction modules, rather than isolated component upgrades. These results validated the effectiveness of MHSF-DETR design in optimizing feature extraction and fusion, offering a highly efficient solution for resource-constrained object detection tasks. [Conclusions] MHSF‑DETR addresses the long‑standing accuracy‑efficiency trade‑off in cotton disease monitoring. By integrating hierarchical perception, adaptive fusion and edge refinement, the model mitigates scale disparity and resource‑constraint challenges. It provides a feasible lightweight template for real‑time diagnosis on agricultural edge devices to support deployment in smart‑farming systems. Future work will expand validation to other plant organs (bolls, stems) and conduct rigorous embedded‑hardware field tests to assess real‑world robustness.

[Objective] Early detection of corn leaf diseases and pests is important for field monitoring, precision pesticide application, and yield protection. In practical field environments, early lesions, insect bodies, eggs, and spore clusters often occupy only a very small part of the image. Their visual appearance is affected by weak texture, irregular shape, and complex backgrounds, making the visual boundary between disease targets and normal leaf tissue less distinguishable. When lightweight detectors based on the YOLO series are applied, fixed convolution kernels lack flexibility for targets with large scale differences. After repeated downsampling, small objects lose representation in deep feature maps, while larger targets dominate the regression loss. These factors make it difficult to balance detection accuracy and deployment efficiency on edge devices. To improve the recognition capability for small targets under complex field conditions, LiteFocus-Net, a lightweight enhancement framework, was developed based on YOLOv11n. [Methods] The framework improved the baseline model from three closely related aspects: adaptive backbone feature extraction, deep feature detail recovery, and scale-aware regression supervision. In the backbone, an Adaptive Kernel Lightweight Block AKL-Block was designed to replace part of the original feature extraction structure. Instead of computing multiple convolution branches simultaneously during inference, AKL-Block used global average pooling and a lightweight gating module to estimate the selection probabilities of three candidate depthwise separable convolution kernels with different receptive fields. A Gumbel-Softmax strategy was used during training to keep the kernel selection process differentiable, while only the kernel with the highest selection probability is activated during inference. In this way, the model could adjust its effective receptive field according to the input feature distribution without introducing the redundant computation usually caused by parallel multi-branch structures. To alleviate the loss of small-object details in deep layers, a Feature Decomposition and Reconstruction module (FDR) was introduced after the P5 feature layer. The deep feature was divided into a structure branch and a detail branch. The structure branch was upsampled by bilinear interpolation to preserve global semantic information, such as leaf shape, lesion distribution, and large disease regions. The detail branch used a lightweight pixel-shuffle reconstruction operation to enhance local responses related to lesion edges, insect contours, and spore textures. The reconstructed feature was then fused with the corresponding P4 feature in the neck network, allowing the model to reuse deep semantic information while selectively strengthening detail-sensitive responses. For the regression loss, a Scale-Aware Gradient Boosting Loss, termed SAGB-Loss, was constructed to increase the training contribution of small targets. The loss combined feature-level weighting and target-area-aware weighting. The feature-level term assigned larger weights to shallow layers that are more closely related to small-object detection, while the area-aware term used a continuous exponential function to smoothly enhance the regression gradients of small targets. Experiments were carried out on a field corn leaf image dataset containing 10 324 images and eight categories of disease and pest targets. The dataset was divided into training, validation, and test subsets at a ratio of 6:2:2. [Results and Discussions] LiteFocus-Net achieved 74.97% mean average precision (mAP) at an Intersection over Union (IoU) threshold of 0.5, with 2.52 million parameters and 5.3 GFLOPs. Compared with the YOLOv11n baseline, the average precision for small objects (AP_s) increased from 28.1% to 32.77%, giving an improvement of 4.7 percentage points, while the computational cost decreased by 15.9%. Ablation experiments confirmed the contribution of each component. AKL-Block reduced the overall computational cost and improved small-target accuracy, FDR further enhanced detail-sensitive features from deep layers, and SAGB-Loss increased the regression contribution of small targets without causing an obvious decline in medium- and large-object detection. Comparisons with Inception-style and selective-kernel multi-scale modules showed that AKL-Block achieved a more favorable accuracy-complexity trade-off under lightweight deployment constraints. Comparisons with full-channel pixel-shuffle reconstruction also indicated that FDR provided a practical balance between detail recovery and additional computational cost. Deployment tests on RK3588 and Jetson Orin NX further showed that LiteFocus-Net maintained real-time inference capability after INT8 quantization, which indicated its potential for field inspection and agricultural edge intelligence applications. [Conclusions] The results suggest that LiteFocus-Net improves small-target detection of corn leaf diseases and pests without relying on a larger model scale. Future work will focus on expanding cross-region, cross-variety, and cross-growth-stage samples, and on exploring region-level dynamic kernel selection, video-based continuous detection, and weakly supervised annotation strategies to improve robustness in long-term field deployment.

[Objective] Data across the agricultural industrial chain are generated throughout production, processing, storage, logistics, sales and supervision. Such data feature dispersed stakeholders, long‑link processes, multi‑source heterogeneity, and high privacy‑ and business‑information sensitivity. Nevertheless, current agricultural big‑data platforms and standalone technologies including blockchain‑enabled traceability, privacy‑preserving computation and federated learning cannot systematically resolve bottlenecks in cross‑entity data circulation, such as data ownership confirmation, controlled utilization, audit tracing and value‑oriented collaboration. Accordingly, this paper proposes a trusted data‑space framework tailored for agricultural production, circulation, supervision and service collaboration scenarios to satisfy requirements for trusted cross‑entity data circulation. It elaborates the framework's layered architecture, core operational mechanisms and implementation paths, offering systematic references for agricultural data sovereignty protection, controlled data sharing, audit tracing and value collaboration. [Methods] Based on the national standard Technical Architecture of Trusted Data Space and the characteristics of the agricultural industry chain, including multiple stakeholders, long-chain processes, limited computing capacity at edge nodes, and data heterogeneity, a domain-adaptation approach was adopted to construct the overall framework and analyze its operating mechanisms. The framework design focused on semantic interoperability, connector-based controlled interaction, privacy-preserving computation, and blockchain-based evidence preservation. A self-developed integrated service platform was then used for preliminary scenario-based analysis, combining about 120 000 food safety sampling and monitoring records from a city during 2023－2025 with the business workflow of an organic farm. The analysis covered standardized data access and governance, risk profiling, on-chain evidence preservation, trusted traceability, permission control, abnormal request interception, and audit tracing. [Results and Discussions] A four-layer architecture was established, consisting of an infrastructure and data resource layer, a trusted data space core layer, a data capability support layer, and an agricultural industry chain application ecosystem layer. The operating mechanism formed a closed loop jointly driven by deep semantic interoperability, connector-based controlled interaction, value co-creation through privacy-preserving computation, and dynamic trust supported by blockchain and smart contracts. Under this framework, ontology models, metadata, and knowledge graphs supported concept alignment, structural mapping, and contextual disambiguation; connectors, digital contracts, and usage control policies constrained usage purposes, invocation frequency, field scope, output forms, and prohibited behaviors; privacy-preserving computation and federated learning enabled cross-entity collaborative analysis without centralizing raw plaintext data; and blockchain recorded contract hashes, log hashes, result digests, and abnormal interception records for auditable tracing. The platform-based scenario description showed that the framework could support standardized data access, risk profiling, on-chain evidence preservation, role-based permission control, abnormal operation interception, and audit tracing in agricultural and food safety risk governance scenarios. In the organic-farm workflow, the mechanism was reflected by keeping raw data off-chain, storing key digests on-chain, controlling data use within authorized environments, and retaining auditable records of the process. Compared with conventional centralized agricultural data platforms, this framework emphasized physical distribution with logical integration and extends security protection from access control to continuous usage control after cross-entity interaction. The four-dimensional implementation pathway further indicated that institutional rules, lightweight connectors and privacy-preserving components, specialized data intermediaries, and progressive pilot deployment should advance in coordination. [Conclusions] The proposed trusted data space framework provides a systematic approach to controlled data circulation, data sovereignty protection, auditability, and value collaboration among multiple stakeholders in the agricultural industry chain without requiring centralized aggregation of raw plaintext data. It can serve as a reference for the circulation of agricultural data elements and the collaborative transformation of the agricultural industry chain, while providing a basis for integration with the national data infrastructure system.

[Objective] The ecological agricultural product industry chain is subject to deep-seated vulnerabilities during scale expansion, quality improvement, and premium growth, yet its structural roots remain inadequately understood. Although digital technologies are expected to relieve these vulnerabilities, recent evidence suggests that digitalization may also induce new suppression loops, resulting in smallholder exclusion, profit squeeze, and homogenized competition. Existing studies have largely addressed this subject from isolated perspectives such as brand governance, industrial chain resilience, or policy evaluation, without offering a systematic diagnosis of the evolutionary mechanism of vulnerability. The aim of this study is to identify the operational logic of growth ceiling archetypes under digital empowerment, exploring how digital technologies interact with feedback structures, and proposing resilience governance strategies that move beyond "technological fixes". [Methods] The Limits to Growth archetype was employed as the analytical framework. Variable selection followed a three-stage procedure integrating theoretical deduction, bibliometric analysis, and case validation. First, constraining factors were identified across multiple dimensions, including natural resource endowment, ecological carrying capacity, market saturation, and trust thresholds, grounded on the theoretical proposition that growth processes endogenously activate restraining forces. Second, a systematic literature search was conducted using search terms such as ecological agriculture, agricultural product industry chain, and system dynamics. Candidate variables that appeared with high frequency in relevant empirical studies were screened, yielding an initial pool of over 20 variables. Third, field investigations of typical cases such as Gannan navel orange, Wuchang rice, and Hengzhou digital jasmine were performed to test, consolidate, and refine the initial pool, ultimately producing 14 core variables. Causal linkages among these variables were established upon three forms of evidence, namely theoretical logic derived from established economic principles, literature evidence drawn from prior empirical findings, and case facts observed during field investigations. Loop polarities were determined by the parity of negative causal chains, where even numbers indicate reinforcing loops (R) and odd numbers indicating balancing loops (B). On this basis, and in combination with field data from the typical cases, four reinforcing loops that drive growth and seven balancing loops that constrain growth were identified. The moderating effects of three categories of digital tools, namely digital agricultural technology platforms, quality traceability systems, and e-commerce platforms, on these loops were then separately assessed. [Results and Discussions] The results indicated that the sustained growth of the ecological agricultural product industry chain was underpinned by four positive feedback loops: the scale-income loop, the quality-demand loop, the premium-income loop, and the quality-reputation loop. At the same time, however, the growth process activated seven negative feedback loops that imposed constraints from multiple directions, including cost erosion, quality dispersion, market saturation, homogenized competition, low-price substitute diversion, trust erosion, and reputation damage—thus locking the system into three types of Limits to Growth dilemmas relating to scale expansion, quality improvement, and premium growth. Digital technology empowerment enhanced the operational efficiency of the industrial chain by reinforcing the positive loops and weakening the negative ones, yet it failed to remove the structural roots of the growth ceiling. More importantly, digitalization unexpectedly triggered three new suppression loops, namely the smallholder exclusion loop, driven by digital and certification barriers, illustrated by the Hengzhou digital jasmine case where elderly flower farmers were marginalized due to the digital divide; the profit squeeze loop, driven by platform cost transference, as shown in the Shanghai Hema village cooperative case, where high equipment costs and loss rates eroded profit margins; and the homogenized competition loop, driven by standardization orientation, as evidenced in the Gannan navel orange case where farmers abandoned flavor-differentiated production practices to comply with platform specifications. These three loops respectively weakened the operational foundations of the scale-income, premium-income, and quality-reputation loops, thereby shifting the locus of vulnerability from traditional constraints to digitally-induced risks. These findings revealed a dual effect of digital technology empowerment, as mitigating certain existing constraints, digital technologies may simultaneously generate new vulnerabilities through digital divides, cost transference, and standardization pressures. The essential task for resilience governance, therefore, lay in identifying and intervening in the dominant balancing loops that constrains growth, rather than relying exclusively on technological inputs. [Conclusions] First, the three limits to growth archetypes, namely scale expansion, quality improvement, and premium growth, constitute the shared structural roots of vulnerability in the ecological agricultural product industry chain. Second, digital technology empowerment cannot eradicate these structural roots and may give rise to new vulnerabilities. Third, enhancing industrial chain resilience should be grounded in leverage point interventions derived from the system's feedback structure. Corresponding governance strategies for the three types of newly identified risks, namely smallholder exclusion, profit squeeze, and homogenized competition, should include lowering technological entry barriers, establishing benefit-sharing mechanisms, and strengthening differentiated certification and geographical indication protection. Overall, this study provides a system dynamics-based analytical tool and policy leverage points for resilience governance of the ecological agricultural product industry chain in the context of digital technology empowerment.

[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.