Thermal and humidity environments can dominate the pig growth, health status, and production performance in pig houses, including air temperature, relative humidity, and airflow velocity. The environment can be regulated to consider the interaction mechanism between housing conditions and pig thermal responses. A mechanistic and physiologically interpretable model is required to accurately simulate pig thermal responses under different thermal and humidity conditions. However, existing models of pig thermal response cannot fully meet the requirements of the intelligent control applications. In this study, a pig two-node heat exchange model (PTHM) was established using biological heat balance theory and thermodynamics. Heat exchange was also simulated among the core, the skin layer, and the surrounding environment. Metabolic heat was generated in the core layer and then transferred to the skin via tissue conduction and blood circulation. Part of the heat was dissipated to the environment as sensible respiratory heat loss. The remaining heat was stored within the body, leading to an increase in rectal temperature. Heat in the skin layer was transferred from the core via conductive transfer and blood-mediated convective transport. The heat was then dissipated to the surrounding environment via convective heat exchange and thermal radiation. A small fraction of heat was dissipated after skin evaporation. Environmental parameters were used as the model inputs, while the major physiological parameters were used as the outputs after simulations. A recognition framework of pig drinking behavior was developed using an improved YOLOv11 object detection architecture, particularly for the prediction accuracy and physiological interpretability of the model. A pig drinking detection model (PDDM) was further established to calculate drinking frequency using this framework. The drinking frequency was then introduced into the PTHM as a behavioral correction factor to regulate blood-mediated convective heat transfer and respiratory heat dissipation, thereby constructing a drinking behavior–corrected pig two-node heat exchange model (D-PTHM). A more realistic representation was obtained for the pig thermoregulation. The results showed that the air temperature was the dominant environmental factor on pig thermal physiological responses. The PTHM model also achieved coefficients of determination (R²) of 0.673, 0.685, and 0.615 for rectal temperature, heart rate, and respiratory rate, respectively. The mean absolute errors (MAE) were 0.320 °C, 7.020 bpm, and 0.916 bpm, while the root mean square errors (RMSE) were 0.412 °C, 9.120 bpm, and 1.635 bpm, respectively. A preliminary representation was obtained for the heat transfer pathway from the body core to the skin. Subsequently, the surrounding environment was offered a simplified representation of whole-body heat balance. The DCB-YOLO drinking detection model achieved a mean average precision (mAP) of 97.47%. The PDDM was used to reliably quantify the pig drinking frequency for behavioral correction of the heat exchange model. The prediction accuracy of D-PTHM was significantly improved after drinking behavior was introduced as a correction factor. The D-PTHM achieved higher R² values of 0.831, 0.771, and 0.775 for the rectal temperature, heart rate, and respiratory rate, respectively. The MAEs were 0.247 °C, 3.358 bpm, and 0.580 bpm, while the RMSEs were 0.332 °C, 4.053 bpm, and 0.747 bpm, indicating the improved model stability and environmental adaptability. The drinking behavior significantly enhanced the mechanistic model to regulate the pig thermal field under different thermal and humidity conditions. This finding can provide a physiologically realistic model for precision environmental control in pig houses. More accurate environmental regulation can be used to improve animal welfare using pig physiological responses in sustainable and efficient livestock production.

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.

Immunization injection is of ten require d to prevent and control the diseases in pig farming. However, current needle injection still relies on manual labor in pig farms, leading to serious challenges, such as high risk of needle breakage, severe cross-infection, low efficiency, time-consuming, and labor-intensive operation, as well as missed or incorrect injections. In this study, a control system was proposed for vision-based positioning and motion in a needle-free injection robot under confined stall breeding scenarios. According to the workflow of swine immunization, the key components were selected to integrate the navigation system of the robot. Its parameters were then determined to improve the needle-free injection module. The motion range of the robotic arm was simulated using MATLAB. The rotation angles of its six axes were constrained to prevent collision between the robotic arm and confined stall. A precise localization was proposed for the needle-free injection site on pigs. The YOLOv8n framework was improved to solve the missed and false detections caused by blurred features of the tail root and susceptibility to dirt interference. The improved network was used to extract the tail root region from pig images. A point set of the hip muscle injection area was constructed for vertical injection. The least squares method (LSM) was applied to fit the surface point set, enabling accurate calculation of the injection point and posture. A control system was developed to accurately track the injection point in the needle-free injection robot, due to the random movement of pigs during operation. According to feeding system, the injection was divided into two phases: a pre-injection and an injection phase. In the pre-injection phase, the tail root position was continuously detected to perform visual servoing control of robotic arm, thereby tracking the injection point in real time. In the injection phase, the robot moved into the injection point, and then performed the injection, according to the last detected pose. The critical distance of phase transition was determined to be 15 cm using depth camera and triangulation. The robot operating system (ROS) was used to integrate navigation, visual detection, and robotic arm control algorithms, enabling the robot to follow a predetermined trajectory and then perform needle-free injection on pigs. Experiments were conducted at a pig farm in Qinhuangdao City, Hebei Province, China. The experimental results showed that the tail root detection algorithm achieved an accuracy of 95.8%, a recall of 93.5%, and a mean average precision (mAP) of 97.1%. The overall injection success rate of the robot was 93.3%, of which 95.2% were vertical injections. The maximum injection deviations in the X, Y, and Z axes were 2.9, 3.7, and 1.9 cm, respectively, corresponding to average deviations of 1.30, 1.91, and 0.59 cm. The injection accuracy was fully met the requirements of swine immunization. A closed-loop control system was realized from chassis navigation, visual recognition, dynamic tracking to precise triggering for the needle-free injection in real scenarios. The findings can also offer the technical and engineering reference for the large-scale intelligent farming.

Cropland water–land balance relationship can be regulated to maintain the cropland productivity, ecological stability, and sustainable land use. It is often required to optimize cropland spatial layout and crop planting structure in sustainable water and land resources, according to “Determining Land Use by Water Availability”. In this study, an integrated analytical framework was developed to explore the spatiotemporal characteristics of the balance between water and cultivated land resources. There was also a close relationship between crop water requirement, water deficit and surplus, drought events, and productivity loss. The research area was taken as the five major agricultural regions in northern China. Meteorological, crop planting structure, and gross primary productivity (GPP) were monthly collected from 2001 to 2022. The full-growing-season crop water requirements of winter wheat, spring maize, and summer maize were estimated using the FAO-56 crop coefficient. A decadal Crop Water Deficit and Surplus Index (CWDI) and its standardized form, the Standardized Crop Water Deficit and Surplus Index (SCWDI), were then constructed to integrate run theory. Empirical orthogonal function (EOF) analysis, correlation analysis, and a Copula–Bayesian conditional probability model were used to systematically identify the Spatiotemporal evolution of cropland water–land balance relationships, crop critical water-demand periods, and productivity loss risks under drought stress. The results showed that (1) multi-year mean full-growing-season water requirements of winter wheat and maize were 500 and 594 mm, respectively, particularly with 627 and 536 mm for spring and summer maize, respectively. Crop water requirements shared significant regional differences. In winter wheat, the water requirement followed the descending order of Huang–Huai–Hai Plain (521 mm) > Loess Plateau (514 mm) > Gansu–Xinjiang Region (467 mm), while spring maize shared the highest water requirement in the Gansu–Xinjiang Region (662 mm). (2) Average drought-event frequency ranged from 0.4 to 2.0 events per year. The Gansu–Xinjiang Region was characterized by a high proportion of extreme drought events, long duration, and high intensity, indicating a cumulative drought pattern, whereas the Huang–Huai–Hai Plain was dominated by high-frequency but short-duration drought events. (3) EOF decomposition showed that the first five modes cumulatively explained 73.7% of the total variance, with EOF1 and EOF2 accounting for 28.6% and 20.0%, respectively, indicating region-wide consistency and regional heterogeneity in interannual dry–wet variations of cropland. (4) Correlation analysis between SCWDI and standardized GPP (SGPP) showed that the key months were identified as July for spring and summer maize, while May for winter wheat. According to the cumulative effect, the water-demand stages of spring maize, summer maize, and winter wheat corresponded to May–June, June, and March–April, respectively. (5) SCWDI–SGPP dependence structures of spring maize and summer maize were best fitted by the Gaussian Copula, while the Frank Copula performed best for winter wheat. The average probabilities of productivity loss were 63%, 80%, and 44%, respectively, for spring maize, summer maize, and winter wheat under extreme drought conditions. The north-central Huang–Huai–Hai Plain and the Loess Plateau were identified as the drought stress conversion into productivity loss. The finding can provide a scientific basis for the optimal cropland layout and crop structure during drought risk prevention in northern China.

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.

Wheel-type rice transplanters have been widely used in mechanized paddy-field transplanting because of their flexible operation and convenient field transfer. However, during paddy-field operation, the unstable adhesion state between the driving wheels and saturated paddy soil may induce lateral deviation, longitudinal slip, and row-spacing errors. To clarify the slip behavior of a wheel-type rice transplanter under different soil moisture contents, traveling speeds, and whole-machine masses, this study analyzed the variation characteristics of the slip ratios of a wheel-type rice transplanter. A Xinyang 2ZG-6D1(G4) wheel-type rice transplanter was selected as the research object. A whole-machine multi-body dynamics model was established in RecurDyn, and a layered paddy-soil particle bed was constructed in EDEM. The Hertz-Mindlin with JKR contact model was adopted as the soil contact model to describe the adhesive contact behavior among paddy-soil particles and between soil particles and soil-engaging components. Compression tests and direct shear tests were conducted to calibrate the discrete element parameters of paddy soil, thereby improving the reliability of the soil model. The bidirectional coupling between the transplanter model and the soil model was realized through the EDEM-RecurDyn coupling interface. Soil moisture content, traveling speed, and whole-machine mass were selected as experimental factors, while lateral slip ratio and longitudinal slip ratio were used as evaluation indexes. Based on the calibrated discrete element method and multi-body dynamics (DEM-MBD) coupling model, a Box-Behnken response surface experiment was conducted to investigate the effects and interaction mechanisms of the three factors on the two slip indexes. Regression models of the lateral and longitudinal slip ratios were established, and multi-objective optimization was carried out to obtain a suitable parameter combination. Field tests were finally performed to verify the prediction accuracy of the coupled simulation model. The results showed that soil moisture content had the most significant effect on both lateral and longitudinal slip ratios, with contribution rates of 20.54% and 35.74%, respectively. The interaction between soil moisture content and whole-machine mass also had an obvious influence on the two slip ratios, indicating that the effect of machine load on wheel-soil interaction depended strongly on the moisture state of paddy soil. With increasing soil moisture content, the bearing, shear, and adhesion characteristics of the soil changed, further affecting wheel sinkage, soil adhesion, and driving stability. Within the experimental range, when the soil moisture content was 32%, the traveling speed was 0.54 m/s, and the whole-machine mass was 854 kg, the lateral and longitudinal slip ratios showed relatively good comprehensive performance; that is, both indexes remained at relatively low levels under the multi-objective optimization constraints. The field validation results showed that the measured lateral slip ratio was 1.55%, while the simulated value was 1.36%, with a relative error of 12.25%. The measured longitudinal slip ratio was 11.48%, while the simulated value was 10.15%, with a relative error of 11.59%. Both errors were within 15%, indicating that the established DEM-MBD coupling model can reasonably predict the slip ratios of a wheel-type rice transplanter under paddy-field conditions. This study provides a feasible simulation method for analyzing wheel-soil interaction in paddy fields and offers a reference for the design of walking systems and the optimization of operating parameters for paddy-field machinery.

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.

Ratoon rice, defined as the production of a second crop from the stubbles remaining after harvest of the main crop, was increasingly promoted in China as a strategy to enhance annual grain yield on limited arable land without additional land preparation, sowing, or transplanting. With the rapid adoption of combine harvesters in major rice-growing regions, mechanized rice ratooning technology with the main crop harvested mechanically became the predominant method in ratoon rice. However, mechanical harvesting introduced critical constraints to ratoon crop. Track-induced crushing, excessive ground pressure, and unstable cutting height frequently caused stubble breakage and axillary bud injury, thereby reducing bud survival, suppressing ratoon tiller production, and compromising yield formation and milling quality in the ratoon crop. These mechanical damages emerged as a primary bottleneck limiting the stable and large-scale adoption of mechanized rice ratooning system. From an agricultural engineering perspective, this review systematically synthesized recent advances in understanding damage mechanisms and in developing technical strategies for loss mitigation, yield stabilization, and quality improvement in mechanized rice ratooning system, with emphasis on machinery optimization, varietal improvement, and integrated agronomic management. Mechanistic studies demonstrated that ground contact pressure and shear forces generated by harvester tracks disrupted stubble structural integrity, damaged vascular tissues, and impeded assimilate translocation to regenerated buds. Under high soil moisture conditions, increased sinkage and soil deformation amplified mechanical stress, substantially reducing bud sprouting rates within track zones compared with non-track areas. In crushed zones, ratoon development often shifted from upper-node buds to lower-node buds with delayed phenology, resulting in reduced canopy uniformity, decreased effective panicle number, and significant yield penalties. Delayed panicle emergence in track areas also led to asynchronous maturity within fields, thereby decreasing head rice rate and increasing variability in milling quality. To quantify mechanical damage, studies adopted indicators including missing-stubble rate, bud survival rate, ratoon tiller-to-panicle conversion rate, yield loss rate, head rice rate, and chalkiness-related parameters. Emerging technologies such as unmanned aerial vehicle remote sensing, machine vision, and in-field sensors were increasingly applied to identify track zones and characterize spatial heterogeneity of mechanical damage, although standardized evaluation protocols remain insufficient. Engineering innovations primarily targeted reductions in crushed area and crushing intensity, as well as improvements in stubble-height uniformity. Advances included lightweight chassis designs with reduced ground pressure, optimized track width and cutting width configurations, automatic header-height control based on multi-sensor perception systems, stubble-righting devices integrated with harvesters, and navigation-assisted path planning to minimize track overlap and headland damage. Although these technologies effectively mitigated mechanical impact and improved ratoon crop performance under experimental conditions, trade-offs among ground pressure, machine stability, fuel consumption, operational cost, and field adaptability limited widespread commercial application. Varietal differences in stem mechanical strength, ratooning ability, and non-structural carbohydrate reserves significantly influenced tolerance to mechanical harvesting stress. Evaluation metrics extended beyond ratoon yield to include bud survival rate, ratoon panicle number, stem morphological traits, and biomechanical properties. Although quantitative trait loci and candidate genes associated with ratooning ability and stem strength were reported, stable loci and deployable molecular markers specifically targeting crushing tolerance remain limited. Complementary agronomic practices, including skip-row planting, pre-harvest drainage to enhance soil bearing capacity, timely post-harvest nitrogen topdressing with balanced phosphorus and potassium inputs, and targeted rehabilitation of track zones, partially alleviated yield and quality losses. Overall, the accumulated evidence indicated that future progress in mechanized rice ratooning system depended on coordinated machine, variety, and agronomy integration, lightweight and intelligent harvester development, standardized damage evaluation systems, and digital monitoring platforms to ensure stable, scalable, and quality-oriented ratoon rice production.

Ecological ditch–pond systems are important measures for controlling agricultural non-point source pollution, yet their practical application is constrained by unstable purification performance and large land occupancy. The effective application of such systems in irrigation districts depends not only on the design and operation management of individual units, but also significantly on their spatial layout (including system area and unit connection pattern). Existing studies often fail to adequately capture the multi-level dynamic responses of water volume and water quality in such systems, which hinders their support for spatial layout optimization. Against this backdrop, this study proposes a system dynamics-based simulation method for optimizing the spatial layout of ditch-pond systems in irrigation districts. A field-ditch-pond system model was developed using the system dynamics simulation tool Vensim, integrating water balance, pollutant removal processes, and hydraulic connections among ditches and ponds. The water depth in the paddy model was determined by inflows, outflows, and water consumption during each time step, while the total nitrogen and total phosphorus concentrations were simulated by considering fertilization, first-order pollutant decay, and inputs from rainfall and irrigation. For the ditch–pond unit model, water volume changes were governed by rainfall, evapotranspiration, seepage, upstream inflow, and drainage discharge. Pollutant concentrations in the ditch–pond unit model were calculated using two modes: static storage-based reduction and dynamic drainage-based reduction. The paddy and ditch–pond unit models were linked through system dynamics into an integrated field-ditch-pond system model, which was calibrated and validated using field monitoring data. A case study was conducted in a typical double-cropping paddy high-standard farmland demonstration area in southern China. The model verification results show that the developed model can effectively simulate the dynamic variations of water volume and pollutant concentrations in the system. The case analysis results show that: 1) With increasing ditch-pond to paddy area ratio, nitrogen and phosphorus removal rates rise, but the rate of increase gradually slows down, suggesting an optimal range of 5%-9%; 2) During the late rice season or under larger area ratios, concentrating wetlands in a single drainage path results in a significantly lower removal rate compared to other layouts; 3) For practical implementation, it is recommended to first determine an appropriate area ratio based on target pollutant reduction goals. Subsequently, wetlands may be placed either at the main drainage outlet or distributed in parallel across different drainage pathways, depending on site-specific conditions. The findings provide a methodological reference for modeling multi-level wetland systems and offer a scientific basis for ecological control of agricultural non-point source pollution. Future work may further refine the simulation of water cycling and pollutant transformation processes within field-ditch-pond systems to enhance model accuracy and applicability.