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Simple Summary: In recent years, the shortage of cow stocks and the reduction of cow breeders in China have caused a shortage of cattle resources and a high price of calves, which has become a bottleneck restricting the further development of China's beef cattle industry. At present, there are few studies on the decision-making behavior of cow breeding in China. In order to explore the factors that affect the decision-making behavior on cow breeding by beef cattle farmers, this paper uses field survey data to make an empirical analysis through the Logit ISM model. The results show that the age of the breeder, the breeding year, the planting area of forage, market expectations, the loan amount, the breeding method policy publicity and policy subsidies are significant factors affecting the decision-making behavior of beef cattle farmers on cow breeding. The beef cattle industry is an important part of herbivorous animal husbandry and an important industry to enrich the dietary structure of residents and promote the income of farmers and herdsmen. The cow is an important foundation to support the healthy development of the beef cattle industry, which is related to the stability of cattle sources and the sustainable development of the industry. However, in recent years, the shortage of cows in our country has led to the shortage of cattle resources and the high price of calves, which has restricted the further development of our beef cattle industry. In order to explore the factors that affect the decision-making behavior behind cow breeding and to find the right policy to mobilize the enthusiasm for cow breeding, based on the field survey of five provinces (cities and regions), including Inner Mongolia Autonomous Region and Ningxia Hui Autonomous Region, this paper conducts an empirical analysis through the Logit ISM model. The results show that market expectation is the surface factor that directly affects the decision-making behavior of cow breeding. The influencing factors of the middle layer are policy propaganda, policy subsidies, breeding years, and feeding methods. The deep root factors are the age of the breeder, the amount of loan, and whether to plant feed crops. Based on this, this paper proposes that we should actively promote the importance of cow breeding, strengthen policy support for cow breeding, formulate scientific and reasonable subsidy policies for cows, innovate financial support methods to solve the problem of fund shortage of farmers, and develop forage resources and reduce the breeding cost of cows. [ABSTRACT FROM AUTHOR]

Simple Summary: We proposed a target detection algorithm based on the channel attention mechanism SENet, the GeLU activation function and layer normalized ShallowSE. We refined and simplified the PANet part and the YOLO Head part in YOLOv4 to obtain the Custom_YOLO target detection module. We designed a 3D coordinate regression algorithm for three fully connected networks in order to predict the goats' coordinates. We combined the improved YOLOv4 target detection algorithm and coordinate regression algorithm to achieve goat localization. In order to achieve goat localization to help prevent goats from wandering, we proposed an efficient target localization method based on machine vision. Albas velvet goats from a farm in Ertok Banner, Ordos City, Inner Mongolia Autonomous Region, China, were the main objects of study. First, we proposed detecting the goats using a shallow convolutional neural network, ShallowSE, with the channel attention mechanism SENet, the GeLU activation function and layer normalization. Second, we designed three fully connected coordinate regression network models to predict the spatial coordinates of the goats. Finally, the target detection algorithm and the coordinate regression algorithm were combined to localize the flock. We experimentally confirmed the proposed method using our dataset. The proposed algorithm obtained a good detection accuracy and successful localization rate compared to other popular algorithms. The overall number of parameters in the target detection algorithm model was only 4.5 M. The average detection accuracy reached 95.89% and the detection time was only 8.5 ms. The average localization error of the group localization algorithm was only 0.94 m and the localization time was 0.21 s. In conclusion, the method achieved fast and accurate localization, which helped to rationalize the use of grassland resources and to promote the sustainable development of rangelands. [ABSTRACT FROM AUTHOR]

Simple Summary: The Chinese Red Steppe Cattle (CRS) is a well-known dual-purpose (meat and milk) cattle breed. Here, the genetic variation and population structure of CRS were studied using 100 K SNP genotyping data. The results show that the genetic structure of CRS is different from other populations, the level of genetic diversity is high, and the level of inbreeding is low. In conclusion, our research provides the genetic basis for the prominent characteristics of CRS, which can be used to improve the breeding program of CRS in the future. Chinese Red Steppe Cattle (CRS), a composite cattle breed, is well known for its milk production, high slaughter rate, carcass traits, and meat quality. Nowadays, it is widely bred in Jilin and Hebei Province and the Inner Mongolia Autonomous region. However, the population structure and the genetic basis of prominent characteristics of CRS are still unknown. In this study, we systematically describe their population structure, genetic diversity, and selection signature based on genotyping data from 61 CRS individuals with GGP Bovine 100 K chip. The results showed that CRS cattle had low inbreeding levels and had formed a unique genetic structure feature. Using two complementary methods (including comprehensive haplotype score and complex likelihood ratio), we identified 1291 and 1285 potentially selected genes, respectively. There were 141 genes annotated in common 106 overlapping genomic regions covered 5.62 Mb, including PLAG1, PRKG2, DGAT1, PARP10, TONSL, ADCK5, and BMP3, most of which were enriched in pathways related to muscle growth and differentiation, milk production, and lipid metabolism. This study will contribute to understanding the genetic mechanism behind artificial selection and give an extensive reference for subsequent breeding. [ABSTRACT FROM AUTHOR]

Simple Summary: Previous studies had reported sex-biased parasitism (SBP) in small rodents. In this study, we investigate the prevalence of six intestinal parasites in Brandt's voles (Lasiopodomys brandtii) that were captured in May, June, July, and August 2022 around the Xilingol Grassland in Inner Mongolia, China. Syphacia obvelata, Aspiculuris tetraptera, and the Trichostrongylidae family were the dominant intestinal parasites for the Brandt's voles captured in the study areas. Season and human activities such as grazing had no significant effect on the infection rates of parasites, but the parasite reproduction level was higher when the ambient temperature was around 18 °C. We found that the sexual size dimorphism (SSD) was ubiquitous in Brandt's voles, and males had bigger body sizes than females. Simple linear regression analysis showed a significant positive correlation between bodyweight and parasite infection rates, so the sex-biased parasitism in Brandt's voles could be explained by the body size hypothesis, as a larger body could provide more ecological niches for parasitic infection. The abundance and prevalence of parasitic infection often vary in different host sexes, and this phenomenon has been named sex-biased parasitism. Brandt's voles are the dominant rodent species in typical steppe habitat and are widely distributed in Inner Mongolia, China, but the prevalence of parasites in Brandt's voles are poorly reported. In this study, we investigated the prevalence of six intestinal parasites in Brandt's voles in May, June, July, and August 2022 around the Xilingol Grassland in Inner Mongolia, China. The results showed that Syphacia obvelata, Aspiculuris tetraptera, and Trichostrongylidae family were the dominant intestinal parasites in Brandt's voles that we captured in this study, and the infection rates of the three parasites were significantly higher in males than females, which showed obvious male-biased parasitism. Season and human activities such as grazing had no significant effect on the infection rates for different parasites, while the parasite reproduction level was higher when the ambient temperature was around 18 °C. Sexual size dimorphism was ubiquitous in Brandt's voles, and it was mainly manifested by the differences in body weight and length between males and females. Simple linear regression analysis showed a significant positive correlation between bodyweight and parasite infection rates, so the sex-biased parasitism in Brandt's voles could be explained by the body size hypothesis, as a larger body could provide more ecological niches for parasitic infection. [ABSTRACT FROM AUTHOR]

Simple Summary: The identification of individual animals is an important step in the history of precision breeding. It has a great role in both breeding and genetic management. The continuous development of computer vision and deep learning technologies provides new possibilities for the establishment of accurate breeding models. This helps to achieve high productivity and precise management in precision agriculture. Here, we demonstrate that sheep faces can be recognized based on the YOLOv3 target detection network. A model compression method based on K-means clustering algorithm and combined with channel pruning and layer pruning is applied to individual sheep identification. In addition, the results show that the proposed non-contact sheep face recognition method can identify sheep quickly and accurately. Accurate identification of sheep is important for achieving precise animal management and welfare farming in large farms. In this study, a sheep face detection method based on YOLOv3 model pruning is proposed, abbreviated as YOLOv3-P in the text. The method is used to identify sheep in pastures, reduce stress and achieve welfare farming. Specifically, in this study, we chose to collect Sunit sheep face images from a certain pasture in Xilin Gol League Sunit Right Banner, Inner Mongolia, and used YOLOv3, YOLOv4, Faster R-CNN, SSD and other classical target recognition algorithms to train and compare the recognition results, respectively. Ultimately, the choice was made to optimize YOLOv3. The mAP was increased from 95.3% to 96.4% by clustering the anchor frames in YOLOv3 using the sheep face dataset. The mAP of the compressed model was also increased from 96.4% to 97.2%. The model size was also reduced to 1/4 times the size of the original model. In addition, we restructured the original dataset and performed a 10-fold cross-validation experiment with a value of 96.84% for mAP. The results show that clustering the anchor boxes and compressing the model using this dataset is an effective method for identifying sheep. The method is characterized by low memory requirement, high-recognition accuracy and fast recognition speed, which can accurately identify sheep and has important applications in precision animal management and welfare farming. [ABSTRACT FROM AUTHOR]