Seasonal reproduction in sheep reduces reproductive efficiency. Melatonin (MT) plays a crucial role in reproductive processes. The purpose of this study was to assess the effects of a 5-day MT implant pretreatment on estrus synchronization and reproductive performance in sheep during seasonal anestrus. A total of 40 multiparous Mongolian sheep were selected and randomly divided into two groups. In the MT group (n = 20), the ewes received an MT implant for 5 days, and then, they were given a progesterone (P4)-containing vaginal sponge for 14 days with equine chorionic gonadotropin (eCG) administered (330 I.U. per ewe; I.M.) at sponge removal. Control (CON) ewes (n = 20) were similarly treated but did not receive MT implants. The results demonstrated that MT implantation significantly improved serum levels of total antioxidant capacity (T-AOC), superoxide dismutase (SOD), catalase (CAT), glutathione (GSH), and glutathione peroxidase (GSH-Px), increased post-ovulatory luteal diameter and serum P4 levels, and reduced ovarian apoptosis. Compared with the CON group, the MT group showed significantly higher pregnancy (68.23% vs. 50.59%) and lambing rates (63.53% vs. 47.06%; number of lambed ewes/number of total ewes) following cervical-timed artificial insemination. Ovarian transcriptome analysis revealed 522 differentially expressed genes (DEGs) in the MT group compared with the CON group, including 355 upregulated and 167 downregulated DEGs. In addition, MT significantly enhanced proliferation and inhibited apoptosis in cultured granulosa cells (GCs) and luteal cells (LCs) in vitro. Moreover, it enhanced the antioxidant capacity of GCs and LCs probably by activating the NRF2 signaling pathway as well as stimulating steroid hormone synthesis. In conclusion, MT implantation 5 days before applying the conventional P4-eCG protocol enhances ovine reproductive outcomes during seasonal anestrus. MT implantation has a beneficial role on the growth and function of ovarian cells. These findings offer novel evidence supporting the functional role of MT in mammalian reproduction, and would be informative for optimizing estrus synchronization in sheep.

Non-contact measurement based on the 3D reconstruction of sheep bodies can alleviate the stress response in sheep during manual measurement of body dimensions. However, data collection is easily affected by environmental factors and noise, which is not conducive to practical production needs. To address this issue, this study proposes a non-contact data acquisition system and a 3D point cloud reconstruction method for sheep bodies. The collected sheep body data can provide reference data for sheep breeding and fattening. The acquisition system consists of a Kinect v2 depth camera group, a sheep passage, and a restraining pen, synchronously collecting data from three perspectives. The 3D point cloud reconstruction method for sheep bodies is implemented based on C++ language and the Point Cloud Library (PCL). It processes noise through pass-through filtering, statistical filtering, and random sample consensus (RANSAC). A conditional voxel filtering box is proposed to downsample and simplify the point cloud data. Combined with the RANSAC and Iterative Closest Point (ICP) algorithms, coarse and fine registration are performed to improve registration accuracy and robustness, achieving 3D reconstruction of sheep bodies. In the base, 135 sets of point cloud data were collected from 20 sheep. After 3D reconstruction, the reconstruction error of body length compared to the actual values was 0.79%, indicating that this method can provide reliable reference data for 3D point cloud reconstruction research of sheep bodies.

Liaoning Cashmere Goats are a high-quality dual-purpose breed valued for both their cashmere and meat. They are also a key national genetic resource for the protection of livestock and poultry in China, with their intensive farming model currently taking shape. Leveraging new productivity advantages and reducing labor costs are urgent issues for intensive breeding. Recognizing goatbehavior in large-scale intelligent breeding not only improves health monitoring and saves labor, but also improves welfare standards by providing management insights. Traditional methods of goat behavior detection are inefficient and prone to cause stress in goats. Therefore, the development of a convenient and rapid detection method is crucial for the efficiency and quality improvement of the industry. This study introduces a deep learning-based behavior recognition and online detection system for Liaoning Cashmere Goats. We compared the convergence speed and detection accuracy of the two-stage algorithm Faster R-CNN and the one-stage algorithm YOLO in behavior recognition tasks. YOLOv8n demonstrated superior performance, converging within 50 epochs with an average accuracy of 95.31%, making it a baseline for further improvements. We improved YOLOv8n through dataset expansion, algorithm lightweighting, attention mechanism integration, and loss function optimization. Our improved model achieved the highest detection accuracy of 98.11% compared to other state-of-the-art (SOTA) target detection algorithms. The Liaoning Cashmere Goat Online Behavior Detection System demonstrated real-time detection capabilities, with a relatively low error rate compared to manual video review, and can effectively replace manual labor for online behavior detection. This study introduces detection algorithms and develops the Liaoning Cashmere Goat Online Behavior Detection System, offering an effective solution for intelligent goat management.

The quality and yield of cashmere closely affect the economic benefits of cashmere goat farming. Studies have shown that controlling light can have an important impact on cashmere but can also affect the concentration of harmful gases. In order to explore the impact of a short photoperiod on the growth of cashmere and harmful gases in goat houses, 130 female (non-pregnant) Shanbei white cashmere goats, aged 4–5 years with similar body weights, were randomly divided into a control group and a treatment group, with 65 goats in each group. The dietary nutrition levels of the experimental goats were the same, and completely natural light was used in the control group; the light control group received light for 7 h every day (9:30–16:30), and the rest of the time (16:30–9:30 the next day) they did not receive light. The light control treatment was carried out in a control house, and the gas content was analyzed. It was found that a shortened period of light exposure could increase the annual average cashmere production by 34.5%. The content of each gas has a certain functional relationship with the measurement time period, but at the same time, we found that the content of NH3 also changes seasonally. In summary, the use of shortened light periods when raising cashmere goats can significantly increase cashmere production and quality, but at the same time, it will increase the concentration of harmful gases in the goat barn, and ventilation should be increased to ensure the health of the goats and the air quality in the barn.