Holistic sidewall ventilation system (HSVS) is characterized by uniform temperature distribution in the poultry house. Yet two challenges remain: low air velocity at the front cross-section and suboptimal positions of recirculation zones in large-scale facilities. The airflow field is governed by the configuration and regulation of air inlets. In this study, the airflow distribution was optimized to enhance overall ventilation performance in HSVS poultry houses. The opening angles of local front inlets were also adjusted using field measurements and numerical simulations. A systematic analysis was implemented to explore the effects of inlet angles on the airflow field. A full-scale HSVS poultry house was selected as the research object, where 118 sidewall air inlets were divided into front, middle, and rear segments (40, 40, and 38 inlets, respectively). Two ventilation scenarios (with 3 and 4 operational fans) were evaluated, wherein the opening angles of the front inlets were adjusted within the range of 10° to 90°, whereas the middle and rear air inlets were kept at fixed angles to match the ventilation scenarios. Air velocity and pressure difference were continuously measured at 6 sensor points in the laying hen activity zone (1.5 m above the ground). Computational fluid dynamics (CFD) incorporated with the Reynolds-averaged Navier-Stokes (RNG) k-ε turbulence model was adopted to simulate the airflow field. The air velocity non-uniformity coefficient was employed as the evaluation metric to quantify the uniformity of airflow distribution. The results showed that different ventilation scenarios displayed an identical variation trend. The opening angle of the front air inlets reduced the static pressure difference between the inlets and the outdoors, while preserving a uniform distribution of pressure difference. The average pressure difference in the middle section of the poultry house was higher than that at the front end, with an average difference of (1.7±0.2) Pa. This discrepancy was also independent of both the inlet opening angle and the ventilation scenario. Meanwhile, air velocity increased with an increase in the opening angle of the front air inlets, indicating a negative correlation with pressure difference. Under the scenario with 3 operational fans, the maximum average air velocity in the front and middle segments reached (0.18±0.02) m/s (at 70°) and (0.30±0.06) m/s (at 90°), respectively; Under the scenario with 4 operational fans, these values were (0.25±0.04) m/s (at 90°) and (0.38±0.06) m/s (at 90°), respectively. Furthermore, the front inlet opening angle was adjusted to alleviate inadequate ventilation in the front section of the house, indicating a moderate improvement in air velocity in the middle section. The air velocity non-uniformity coefficient decreased consistently, as the inlet angle increased, thus dropping to below 0.30 at the angle of 70° or larger (0.24 for 3 fans and 0.30 for 4 fans at 70°). Subsequently, two optimal operating conditions were selected for further CFD simulation analysis. CFD simulation results showed that the front inlet angles enhanced the overall indoor air velocity, where the average air velocity at the front cross-section increased by 0.10 m/s. The optimal inlet angles effectively mitigated front ventilation dead zones for the airflow uniformity, indicating less unfavorable recirculation zones. The opening angle of the front air inlets was adjusted to 70° or larger for the weak ventilation zone at the front of HSVS poultry houses, indicating indoor airflow uniformity. Zoning regulation of air inlet angles can offer a cost-effective and efficient solution to enhance the ventilation performance of HSVS poultry houses. The finding can provide the theoretical basis and technical support for environmental control optimization in large-scale poultry houses. Future research can be expected to integrate the heat and mass exchange between hens and the environment in smart agriculture.

Near-surface water vapor condensation is one of the most crucial steps to fully utilize atmospheric water sources in ecological agriculture. It is often required to clarify the dynamic relationship between near-surface water vapor and condensation. This study aims to investigate near-surface water vapor dynamics and their response to condensation events in arid regions. Three geographical conditions of northwest China were selected to capture the meteorological parameters, including the southeast margin of the Tengger Desert (TD), the arid belt of the center in Ningxia Hui Autonomous Region (CANX), and the semi-arid region in Ningxia Hui Autonomous Region (SANX). The hydrostatic integration was employed to calculate water vapor flux and content within a 100 m range above ground at each observation point, based on the evolution patterns of meteorological factors with hight at the Yinchuan radiosonde station. The leaf wetness sensor of PHYTOS31 was used to calculate the condensation water amount at 5 cm above ground. A correlation analysis was performed on the water vapor, condensation water, and meteorological parameters. The results indicate that there were significant differences in annual total condensation water at TD, CANX, and SANX sites (P<0.05), with annual averages of 13.35, 22.68, and 32.80 mm, respectively, during the observation period. Spatiotemporal variations in water vapor flux and content were significant (P <0.05) at all three stations, thus peaking in summer and declining in winter. Minimum monthly water vapor flux at TD, CANX, and SANX were 1.9, 2.3, and 1.8 kg/(m·s), respectively, while minimum monthly water vapor content was 1.30, 1.45, and 1.60 mm, respectively. Peak water vapor flux and content occurred in July at SANX, and in August at TD and CANX. Water vapor flux was markedly higher at the southeast margin of the Tengger Desert and arid region than that in the semi-arid regions, with 21.2, 19.4, and 13.9 kg/(m·s) for the maximum TD, CANX, and SANX, respectively. The monthly average water vapor content was highest at the SANX site (13.69 mm), while those were 11.35 and 11.23 mm, respectively, at the TD and CANX sites. The primary wind direction ranges influencing water vapor flux and content at the three stations were: TD with 0°–60° and 150°–210°, CANX with 180°–240°, and SANX with 0°–30°, 120°–240°, and 300°–359°. In terms of a single condensation event, both water vapor flux and content decreased during the condensation accumulation phase, whereas there was an increase when the condensation dissipated. Condensation content showed a significant negative correlation with water vapor content (P<0.05). The correlation coefficients for TD, CANX, and SANX were −0.652, −0.751, and −0.722, respectively. Water vapor flux first decreased and then increased during the diurnal cycle without condensation, due to the absence of water vapor phase change. While water vapor content shared an increasing trend. Water vapor flux and content exhibited a significant negative correlation (P<0.05) during the process. These findings can also provide valuable insights to characterize near-surface water vapor dynamics under diverse geographical conditions.

Recognition accuracy of small targets is often required for dense fruit distribution in natural orchard environments. Therefore, a flat peach detection model based on an improved YOLOv8 architecture, termed CCGs-YOLO, was proposed in this study. The proposed model integrates the MetaFormer framework with a convolutional gated linear unit module. A hybrid module combining convolution and attention mechanisms was introduced to enhance spatial feature extraction and improve feature representation under complex background conditions. Meanwhile, a channel-aware module was incorporated to simulate inter-channel dependencies, thereby improving the discrimination capability between fruit targets and cluttered backgrounds. Specifically, the C2f_ConvFormer module was employed to simultaneously capture local and global contextual information, while the C2f_CaFormer module was introduced to enhance channel interaction and feature aggregation. In addition, a convolutional gated linear unit mechanism was embedded into the network to improve feature selection capability and robustness against background noise. To address the small object detection problem in densely distributed fruit scenarios, localization accuracy was further improved. An optimized regression loss function based on an inner-overlap constraint, named Inner-CIoU, was adopted to achieve more accurate bounding box regression and reduce localization errors caused by overlapping targets. Experimental results demonstrated that the ConvFormer module improved the F1-score to 90.44% and the mAP50 to 96.40%, indicating enhanced feature extraction capability. The CaFormer module increased the F1-score from 89.96% to 90.63%, while the mAP50 further improved to 96.12%, demonstrating effective channel modeling capability under relatively high inference efficiency. When only the convolutional gated linear unit mechanism was applied, the F1-score reached 90.20% and the mAP50 achieved 96.12%, verifying its effectiveness in enhancing feature representation. Furthermore, the combination of CaFormer and convolutional gated linear unit improved the Precision to 91.47%, the F1-score to 90.77%, and the mAP50 to 96.24%, demonstrating the complementary advantages of channel modeling and gated feature selection. In terms of localization performance, Inner-CIoU improved both mAP and model convergence stability compared with the conventional CIoU loss function. After integrating all improved components, the model achieved relatively better overall performance. Precision reached 93.07%, representing an increase of 3.31 percentage points compared with the baseline model. The F1-score reached 90.87%, while the mAP50 achieved 96.24%. Meanwhile, the model size was reduced from 5.97 MB to 4.88 MB, and the number of parameters decreased from 3.01 million to 2.42 million, indicating that the proposed model possesses favorable lightweight characteristics while maintaining relatively high inference speed of 362.07 FPS. In addition, comparative experiments were conducted with several mainstream models, including different versions of the YOLO series. Under challenging scenarios such as occlusion, small targets, dense distribution, and edge targets, the proposed model achieved relatively superior performance in terms of Precision, F1-score, and mAP, demonstrating improved detection stability and feature perception capability. Visualization analysis further indicated that the improved model could focus more accurately on fruit regions and suppress background interference to a certain extent. Furthermore, deployment experiments on edge computing devices demonstrated that the proposed model could still maintain relatively high detection accuracy and stable performance under practical application conditions, with the mAP50-95 reaching 88.82%, indicating potential for real-world applications. Overall, the proposed model effectively balanced detection accuracy, model lightweight characteristics, and computational efficiency, demonstrating good robustness and adaptability in complex orchard environments. The proposed approach can provide a feasible technical solution for rapid and accurate fruit recognition in flat peach harvesting.

Conventional feeding of lactating sows in commercial farrowing units was often constrained by coarse feed control, fixed meal timing, and feed losses caused by rooting and manipulation at the trough. These limitations frequently resulted in unstable feed intake during mid- to late lactation, which in turn increased the likelihood of excessive body-reserve mobilization and delayed reproductive recovery after weaning. To address these problems, this study adopted a two-stage progressive experimental design to systematically evaluate the effects of an intelligent precision feeding system on sow feed intake, body condition, reproductive efficiency, and piglet growth performance, and to identify key operating parameters. The study was conducted in a commercial farrowing facility in Shaanxi, China, from March to June 2025. The intelligent feeding unit integrated controlled-dose dispensing with event-based data logging and a trigger mechanism that delivered small top-up portions when trough residual feed and short-window interaction signals indicated persistent feeding motivation; a water–feed mixing option was implemented through a water-control module. Daily feed intake and daily water consumption were recorded automatically. Sow body condition was assessed using backfat thickness measured at entry to the farrowing crate and at weaning. Reproductive recovery and piglet performance traits were obtained from the farm production-record system. In Experiment I, ninety-four multiparous sows were allocated to an Intelligent Feeding Group (IFG) or a Traditional Feeding Group (TFG) (47 sows per unit) housed in environmentally comparable units. IFG sows were managed using the hybrid logic combining a stage-wise baseline allowance with sow-initiated triggered top-ups, whereas TFG sows were fed with a conventional dry-feed feeder at fixed times (three meals per day). The intelligent system produced more stable intake trajectories, and the clearest separation between treatments emerged during mid- to late lactation. From approximately day 15 postpartum onward, daily feed intake in IFG exceeded that of TFG by about 5%-10%, reflecting a more sustained intake plateau and a later decline toward the end of lactation. Consistent with improved intake stability, body-reserve mobilization was reduced in IFG as indicated by backfat change: mean backfat loss decreased from 2.61 mm in TFG to 1.69 mm in IFG (P<0.01), corresponding to a 35.2% reduction. Reproductive recovery was accelerated in IFG. The weaning-to-estrus interval (WEI) shortened from 9.04 days (TFG) to 8.15 days (IFG) (P<0.01), and the wean-to-service rate increased from 85% to 90%. Piglet outcomes also improved in association with the stabilized maternal intake pattern: average weaning weight increased from 7.30 kg (TFG) to 7.68 kg (IFG) (P<0.01), pre-weaning weight gain rose from 6.06 to 6.47 kg (P<0.05), and pre-weaning survival increased from 89.13% to 92.06%. Together, these results indicated that the intelligent feeding approach supported higher and more persistent feed intake during late lactation and aligned with improved body-condition preservation and superior reproductive and litter performance. In Experiment II, parameter screening was performed within two units equipped with the intelligent feeding system to compare two deployable “meal frequency × dilution” strategies under identical hybrid logic. A five-meal strategy with a water-to-feed ratio of 1.3:1 during mid- to late lactation was compared with a four-meal strategy with a water-to-feed ratio of 1.5:1. The five-meal strategy with a water-to-feed ratio of 1.3:1 maintained a higher and more persistent intake plateau between days 12 and 21 postpartum and delayed the late-lactation decline compared with the four-meal strategy with a water-to-feed ratio of 1.5:1. Daily water consumption showed a similar temporal pattern, with a sharper late-lactation decline under the four-meal strategy with a water-to-feed ratio of 1.5:1. Backfat-change outcomes during parameter screening were consistent with these temporal intake differences. The five-meal strategy with a water-to-feed ratio of 1.3:1 showed a more favorable backfat-loss profile, with smaller loss and a more concentrated distribution with fewer extreme negative values. Because meal frequency and water-to-feed ratio were coupled in this screening comparison, mechanistic interpretation was limited to the combined strategy rather than isolated main effects. In conclusion, a sensor-based, trigger-activated phased precision feeding approach provides a practical and traceable framework for stabilizing feed and water intake during mid- to late lactation in commercial farrowing systems. This approach reduces body-reserve mobilization, as indicated by lower backfat loss, supports faster post-weaning reproductive recovery, and improves piglet growth and survival under the tested conditions. Within the evaluated operating settings, the five-meal strategy with a water-to-feed ratio of 1.3:1 represents a promising deployable configuration for sustaining the late-lactation intake plateau and mitigating the end-of-lactation decline in intensive swine production.

Fall armyworm (Spodoptera frugiperda) is one of the most serious pests in maize fields. It is often required to early and accurately detect its infestation for timely and effective pest prevention using unmanned aerial vehicle (UAV) imagery. However, reliable detection has been confined to the challenges: 1) The small and subtle feeding marks caused by the larvae, leading difficult to identify at high altitudes. 2) Consistent recognition has been limited to significant variations in object scale at different flight heights. 3) The accurate detection has also been limited to the low contrast between damaged leaf tissue and surrounding healthy foliage, especially under the different lighting and environmental conditions in fields. Collectively, advanced computer vision is necessary to robustly identify early signs of infestation at diverse scales under complex backgrounds. In this study, a robust deep learning model was developed to reliably identify the subtle infestation traces in multi-scale UAV images. A detection architecture, termed coordinated-BiFPN-P2-YOLO (CBP-YOLO), was also proposed using YOLOv8. Real-enhanced super-resolution generative adversarial network (Real-ESRGAN) was applied as a preprocessing step to reduce image degradation from low ground sampling distance. High-fidelity textures of leaf damage were reconstructed from original low-resolution inputs. The backbone of YOLOv8 was enhanced with the Coordinated attention (CA) mechanism. Spatial and channel-wise features were captured to improve the localization and discrimination of minute lesions. Furthermore, the neck component was upgraded with a Bi-directional feature pyramid network (BiFPN) for the highly efficient top-down and bottom-up cross-scale feature fusion. Information loss was minimized for consistent representation during hierarchical propagation at different scales. In addition, a detection head was added to specifically strengthen sensitivity to small targets, particularly at a 160×160 spatial resolution with 64-channel output. The improved model was trained and then evaluated on the custom UAV dataset, which was collected from maize fields naturally infested by fall armyworm under diverse lighting conditions and flight heights. Extensive experiments demonstrated that the CBP-YOLO achieved a peak performance on the imagery with a ground sampling distance (GSD) of 0.38 cm per pixel. Real-ESRGAN significantly alleviated texture blurring and edge ambiguity in low-resolution images, leading to better delineation of feeding scars. Ablation studies were conducted to evaluate the effectiveness of the improved model. There was an outstanding performance on the UAV multi-scale blade dataset. Specifically, there was an average precision (AP@0.5) of 76.5%, which increased by 3.4 percentage points, compared with the baseline model. The robustness and practical applicability of the improved model were obtained in the blades of varying scales during aerial inspection. A comparison showed that the CBP-YOLO outperformed state-of-the-art detectors—including YOLOv9 medium, YOLOv10 medium, YOLOv11 medium, Faster region-based convolutional neural network, and RetinaNet—by margins of 10.1, 7.2, 5.1, 9.3, and 17.9 percentage points in AP@0.5, respectively. Notably, the high precision was also maintained under varying illumination and partial occlusion, indicating strong generalization in agricultural environments. The improved CBP-YOLO framework effectively detected subtle, multi-scale fall armyworm infestation signals during UAV monitoring. Superior accuracy and robustness of the improved model were achieved to synergistically combine super-resolution enhancement, attention-aware feature extraction, fine-grained detection heads, and bidirectional multi-scale fusion. These findings can also provide a practical and scalable solution for early pest outbreak detection, thereby enabling timely intervention to reduce the crop losses in large-scale maize production.

Multi-tier perching layer houses can offer hens with perches, nests, and multi-level activity spaces. But their complex spatial structure and airflow configuration can also lead to local environmental differences and short-term fluctuations. It is often required for the accurate short-term prediction of indoor environmental parameters under proactive ventilation control and environmental risk warning in cage-free laying hen production. This study aimed to develop a short-term prediction for indoor temperature, relative humidity, and ammonia concentration in a multi-tier perching layer house, according to multi-point monitoring data. Continuous environmental data were collected from one experimental multi-tier perching layer house. The indoor monitoring points were arranged in different perching areas, while one outdoor monitoring point was used to represent boundary environmental conditions. Time alignment and resampling at a 20 min interval, point-wise mean values, and spatial ranges were calculated after data cleaning to evaluate environmental differences among monitoring points. Pearson correlation analysis was used to examine the relationship among temperature, relative humidity, and ammonia concentration. A short-term prediction framework was then developed using gradient boosting regression. Historical observations, outdoor environmental information, and indoor–outdoor difference features were used as model inputs for temperature and relative humidity. A multivariable prediction model was constructed to combine ammonia historical concentration with temperature and relative humidity. The most recent 7 d data was used as an independent validation set. In addition, recursive prediction was performed to evaluate the model performance for a future 24 h horizon. Furthermore, 0.05 and 0.95 quantile regression models were established to generate 90% prediction intervals. The results showed that the indoor environmental parameters differed among monitoring points. The average temperature, relative humidity, and ammonia concentration were 18.99-20.88 ℃, 46.52%-51.98%, and 1.32-1.86 mg/m³, respectively, at different indoor monitoring points. The mean spatial ranges were 2.31℃, 11.84%, and 1.27 mg/m³, respectively, indicating that the parameters varied among different perching areas. Correlation analysis showed that temperature and relative humidity were negatively correlated with ammonia concentration, with correlation coefficients of −0.26 and −0.16, respectively. Ammonia concentration was dominated by its historical state, thermal and humidity conditions, as well as ventilation. In the independent validation set, the temperature prediction model achieved a coefficient of determination of 0.96, a root mean square error of 0.73 ℃, and a mean absolute error of 0.50 ℃, whereas those values were 0.97, 2.94%, and 1.77%, respectively, in relative humidity. In ammonia concentration, the multivariable prediction model achieved a coefficient of determination of 0.76, a root mean square error of 0.44 mg/m³, and a mean absolute error of 0.27 mg/m³. In the multivariable model, the coefficient of determination increased from 0.64 to 0.76, whereas the mean absolute error reduced from 0.37 to 0.27 mg/m³, compared with the univariate gradient boosting regression model with only ammonia historical information. As such, temperature and relative humidity features provided useful supplementary information for ammonia prediction. The 24 h recursive prediction showed that the stable prediction performance was maintained during continuous forecasting. Smooth prediction curves were produced without outstanding abnormal jumps. The 90% prediction intervals were also provided to quantify the fluctuation range of environmental parameters. The short-term prediction can be used to adjust proactive ventilation for the less ammonia risk environment in multi-tier perching layer houses.

The meat duck industry in China has contributed over 82% of the total slaughter volume worldwide. Therefore, an optimal air temperature is often required for the comfort and survival of meat ducks during breeding. However, the air temperature is susceptible to multiple factors, such as relative humidity and illumination intensity. It is a high demand to timely and accurately predict air temperature for the high-density healthy breeding. However, conventional temperature prediction has been limited to low accuracy, robustness, and generalization. In this study, a hybrid 1DCNN-BiLSTM-DQN model was proposed to integrate with a one-dimensional convolutional neural network (1DCNN), a bidirectional long short-term memory network (BiLSTM), and a deep Q-network (DQN). Duck-house temperature was accurately predicted after model construction. The temperature time-series signal was also decomposed into high- and low-frequency components via the discrete Fourier transform (DFT). Given that the high-frequency component represented short-term fluctuations, the 1DCNN was used to extract local features from the high-frequency component; whereas the low-frequency component represented long-term fluctuations, the BiLSTM was used to extract long-sequence dependency features from the low-frequency component. Subsequently, the two sets of features were fused using a concatenation model. And finally, the temperature prediction value was obtained after the mapping of a fully connected layer. Furthermore, the DQN algorithm was introduced to construct an agent for iterative optimization of the hyperparameters. An adaptive mechanism was optimized with an 8-dimensional state space and a 12-dimensional action space, enabling dynamic optimization of key hyperparameters, such as the learning rate, number of hidden layers, dropout rate, and network architecture. Thereby, the prediction robustness of the model was improved under scenarios of seasonal transitions and extreme weather. The indoor and outdoor temperature data of net-raised duck houses were collected in Gaoyou City, Yangzhou City, Jiangsu Province, from March 22, 2025, to March 22, 2026. The results demonstrated that the 1DCNN-BiLSTM-DQN model achieved a coefficient of determination (R²) of 0.993 with an optimal input step size of 48. The MAE and RMSE were superior to the conventional models, such as the Temporal Convolutional Network and Transformer. Specifically, the 1DCNN-BiLSTM-DQN model exhibited the following improvements under different weather conditions: On cloudy days, the MAE and RMSE decreased from 0.29 °C to 0.18 °C, and from 0.36 °C to 0.23 °C, whereas the R² increased from 0.77 to 0.91; On sunny days, the MAE and RMSE decreased from 0.48 °C to 0.23 °C, and from 0.59 °C to 0.31 °C, whereas the R² increased from 0.79 to 0.94; On rainy days, the MAE and RMSE decreased from 0.54 °C to 0.33 °C, and from 0.70 °C to 0.46 °C, whereas the R² increased from 0.72 to 0.89. The combined 1DCNN-BiLSTM prediction model achieved better prediction performance compared with the conventional models, such as BiLSTM, 1DCNN, TCN, and Transformer. In summary, the 1DCNN-BiLSTM-DQN model can be expected to predict the temperature in duck houses. The findings can also provide data support for early environmental regulation, thereby reducing the risk of environmental stress in meat ducks.

Strong agricultural towns have promoted the circulation of resources among towns in modern rural China. Local resources can be integrated to develop comparatively advantaged leading industries in sustainable agriculture. This study aims to clarify the spatial distribution and influencing factors of strong agricultural towns with various categories at the national level from 2018 to 2024. Nine categories were classified according to the leading industries. A combination of spatial analysis, including the average nearest neighbor index, kernel density estimation, and geographical detector, was adopted to explore the spatial pattern and driving factors. The results show that: (1) A total of 1 709 strong agricultural towns were approved in China, which were distributed in the third topographic step along water sources. The overall uneven spatial distribution exhibited a “northeast–southwest” pattern. Kernel density analysis revealed that the towns specializing in different product categories exhibited the distribution patterns of "category-region matching and core-led radiation". Among them, the largest number of grain and oil industry-strong towns reached 389, while edible fungus industry-strong towns were the smallest, with only 60. Overall, three major high-density clusters were formed in the border areas of Hebei-Shandong-Henan, Jiangsu-Zhejiang, and Sichuan-Chongqing. (2) At the provincial level, the conventional major agricultural provinces—Shandong, Sichuan, and Henan—shared a large number of such towns, indicating the sound quantity and spatial layout. According to the average nearest neighbor index and distribution density, 17 provinces shared the high dense distribution. Specifically, the dense agglomeration was found in the six regions (Shandong, Henan, Guangdong, Jiangsu, Hubei, and Chongqing); Beijing, Tianjin, and Shanghai were the dense uniformity; Three autonomous regions (Xinjiang, Inner Mongolia, and Tibet) presented the scattered agglomeration. (3) The geographical detector showed that the agricultural production scale and regional economic development level served as the significant single factors to explain the distribution of strong industry towns. Among them, the total output value of agriculture, forestry, animal husbandry, and fishery also presented the strongest explanatory effect, particularly for the agricultural development level and optimal industrial structure. In contrast, regional and policy factors shared the weak independent explanatory effects, with the stronger explanatory power after interactions with the other factors. In conclusion, the strong agricultural towns can be expected to position product categories, according to local resources, differentiated development, and comparative advantages. Planning and layout can promote the clustered development of factor efficiency in strong agricultural towns, leading to their differentiated, intensive, and high-quality development. The findings can also provide data support to construct strong industry towns.

Water scarcity has long constrained agricultural sustainability in the Huang-Huai-Hai Plain, a vital grain production base in China. Regional water resources can also be regulated to improve water use efficiency in sustainable agriculture. It is often required to precisely assess agricultural water use efficiency. Crop production water footprint can be expected to measure the sustainability and efficiency of water resource utilization during the entire crop growth cycle. This study selected winter wheat as the research subject. Assimilated variables were utilized as remotely sensed leaf area index (LAI) and soil moisture (SM). A quantitative assessment was also developed for winter wheat water footprint, according to dual-variable assimilation of crop models and remote sensing data. Spatial dependency and clustering of winter wheat water footprint were then determined using spatial autocorrelation analysis. Furthermore, winter wheat yield–total water footprint quadrant classification, blue and green water resource dependency, and groundwater extraction proportion were integrated to clarify regional water source dependence and formulate differentiated water footprint management strategies. The results indicated that: 1) Data assimilation significantly improved the accuracy of the WOFOST model to simulate the winter wheat yield. There was strong consistency between the simulation and the statistical yield after data assimilation, with an R² increased to 0.98 and an RMSE reduced to 67.68 kg/hm². The accuracy significantly also improved after simulation, compared with an R² of 0.42 and an RMSE of 566.78 kg/hm²; 2) The average green, blue, and total water footprint of winter wheat were 0.35, 0.30, and 0.65 m³/kg, respectively, after data assimilation. The green and the total water footprint exhibited a spatial distribution pattern higher in the south and lower in the north, while the blue water footprint showed a pattern higher in the north and lower in the south; 3) Spatial autocorrelation of winter wheat green and blue water footprint was stronger than that of the total water footprint. The blue, green, and total water footprint of winter wheat exhibited significant spatial clustering, primarily characterized by high-high and low-low clustering; 4) The northern region should prioritize stable production, water saving regulation, and reduction of groundwater extraction, whereas the southern region should focus on improving precipitation use efficiency. This finding can provide scientific support and decision-making basis for the refined and differentiated water resource strategies in typical water-scarce agricultural regions, such as the Huang-Huai-Hai Plain. A solid theoretical foundation and technical framework can help allocate agricultural water resources at the regional scale.

Ecosystem services supply and demand can coordinate the socioeconomic and environmental systems for the regional ecological security. Their spatiotemporal dynamics can be used to identify the complex interactions among services under anthropogenic disturbance. In this study, six ecosystem services were assessed in Chongqing from 2005 to 2023. The InVEST model and Spearman correlation analysis were employed to quantify carbon sequestration, grain production, soil conservation, water yield, habitat quality, as well as recreation and leisure. Furthermore, a systematic analysis was also conducted on the spatiotemporal dynamics of their supply-demand relationships and trade-offs/synergies over urbanization zoning. The results indicate that: (1) Although Chongqing maintained a surplus of ecosystem services, the margin of the surplus consistently narrowed over the study period. The supply-demand ratio was slight but persistently declined. High supply-demand ratios for carbon sequestration, water yield, soil conservation, habitat quality, as well as recreation and leisure were concentrated in the northeastern and southeastern areas of Chongqing, whereas the low ratios were clustered in the key districts of the main metropolitan area. High values of grain production were distributed in the peripheral counties of the metropolitan region, with deficits in the urban, the northeastern, and southeastern parts of Chongqing. (2) Urbanization exhibited a monocentric-polycentric structure with the west-east gradient, which was characterized by urban expansion and concomitant rural contraction. There was an outstanding spatial gradient in the supply and demand of ecosystem services. Among them, the demand increased progressively from rural to urban areas, while the supply exhibited the inverse pattern. The rural areas were positioned as primary provisioning zones, while the urban as persistent deficit zones. Supply-demand ratios declined for most services; Urban expansion zones transitioned from a balance to a deficit, with the notable exceptions of soil conservation and water yield, indicating the increased surpluses. (3) Static relationships among ecosystem services in rural areas were dominated by strong, stable trade-offs or synergies. In urban-rural transition zones, the weak synergies prevailed and fluctuated significantly, whereas urban expansion zones exhibited a trade-off between synergy dominance. Only five service pairs exhibited consistent trade-off or synergy relationships with the minimal temporal variation in urban areas. Habitat quality was persistently synergistic with recreation and leisure over all urbanization zones. While water yield consistently traded off with recreation and leisure. Most dynamic relationships among services were not statistically significant in rural and urban areas. There was a pronounced differentiation in the urban-rural transition zones, whereas the trade-offs and synergies were in urban expansion zones; Soil conservation and water yield remained synergistic over the period. The strength of the synergy increased in urbanization intensity, whereas habitat quality and water yield remained persistently in trade-off. (4) Differentiated strategies were proposed to realize the divergent patterns of ecosystem services supply-demand and their interactions in the urbanization zones. In urban areas, ecological potential should prioritize enhancing the ecological network connectivity. Urban expansion and urban-rural transition zones are required for the enforcement of ecological redlines, particularly with context-specific natural restoration and topographic constraints. In rural areas, ecotourism and carbon sink markets can be coupled with the urban-rural ecological compensation. Ecological assets can also be translated into tangible values. Differentiation of urbanization zones in the mountainous metropolitan can be used to examine the spatial heterogeneity of ecosystem service dynamics along the urban-rural gradient. This finding can provide a robust scientific foundation to inform urban spatial and sustainable ecosystems in Chongqing.