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Simple Summary: Weaning stress affects the health of calves, inhibiting growth and disrupting gastrointestinal function. Benzoic acid (BA) is known for promoting growth and intestinal health. This study aims to investigate the effects of BA on the growth performance, rumen fermentation, and rumen microbiota of weaned Holstein dairy calves. Thirty-two Holstein dairy calves (60 days of age) were randomly divided into four groups (n = 8) supplemented with 0%, 0.25%, 0.50%, and 0.75% BA to the basal diet (dry matter basis) for 42 days, respectively. The results showed that supplementation with 0.50% BA promoted growth performance by increasing feed intake and average daily gain and reducing feed-to-gain ratio, improved rumen fermentation parameters, and increased the relative abundance of probiotics in the rumen. This suggests that 0.50% BA supplementation might be an effective method for the efficient breeding of weaned calves. Supplementation with benzoic acid (BA) in animal feed can reduce feeds' acid-binding capacity, inhibit pathogenic bacterial growth, enhance nutrient digestion, and increase intestinal enzyme activities. This study aimed to investigate the effects of different doses of BA on the growth performance, rumen fermentation, and rumen microbiota of weaned Holstein dairy calves. Thirty-two Holstein calves at 60 days of age were randomly assigned into four groups (n = 8): a control group (fed with a basal diet without BA supplementation; CON group) and groups that were supplemented with 0.25% (LBA group), 0.50% (MBA group), and 0.75% (HBA group) BA to the basal diet (dry matter basis), respectively. The experiment lasted for 42 days, starting at 60 days of age and ending at 102 days of age, with weaning occurring at 67 days of age. Supplementation with BA linearly increased the average daily gain of the weaned dairy calves, which was significantly higher in the LBA, MBA, and HBA groups than that in the CON group. The average daily feed intake was quadratically increased with increasing BA supplementation, peaking in the MBA group. Supplementation with BA linearly decreased the feed-to-gain (F/G) ratio, but did not affect rumen fermentation parameters, except for the molar proportion of butyrate and iso-butyrate, which were linearly increased with the dose of BA supplementation. Compared with the CON group, the molar proportions of iso-butyrate in the LBA, MBA, and HBA groups and that of butyrate in the HBA group were significantly higher than those in the CON group. Supplementation with BA had no significant effect on the alpha and beta diversity of the rumen microbiota, but significantly increased the relative abundances of beneficial bacteria, such as Bifidobacterium, and reduced those of the harmful bacteria, such as unclassified_o__Gastranaerophilales and Oscillospiraceae_UCG-002, in the rumen. Functional prediction analysis using the MetaCyc database revealed significant variations in the pathways associated with glycolysis across groups, including the GLYCOLYSIS-TCA-GLYOX-BYPASS, GLYCOL-GLYOXDEG-PWY, and P105-PWY pathways. In conclusion, BA supplementation improved the composition and function of rumen microbiota, elevated the production of butyrate and iso-butyrate, and increased the growth performance of weaned Holstein dairy calves. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we address the inefficiencies and animal welfare concerns associated with the traditional manual measurements of cattle dimensions by introducing a non-contact, automated measurement method. This method utilizes an improved keypoint detection model coupled with unilateral depth imaging technology. By improved the keypoint detection model, we have improved the model's capability of processing critical cattle features. Subsequently, cattle body keypoints identified through conditional filtering of the depth image are optimized. Finally, these keypoints are integrated with various algorithms to compute the body size parameters of the cattle. In tests conducted on 23 beef cattle, the mean relative errors for body height, lumbar height, body length, and chest girth were 1.28%, 3.02%, 6.47%, and 4.43%, respectively. This research is of great significance for enhancing animal welfare and contributes to the sustainable development of modern livestock farming. Traditional measurement methods often rely on manual operations, which are not only inefficient but also cause stress to cattle, affecting animal welfare. Currently, non-contact cattle dimension measurement usually involves the use of multi-view images combined with point cloud or 3D reconstruction technologies, which are costly and less flexible in actual farming environments. To address this, this study proposes an automated cattle dimension measurement method based on an improved keypoint detection model combined with unilateral depth imaging. Firstly, YOLOv8-Pose is selected as the keypoint detection model and SimSPPF replaces the original SPPF to optimize spatial pyramid pooling, reducing computational complexity. The CARAFE architecture, which enhances upsampling content-aware capabilities, is introduced at the neck. The improved YOLOv8-pose achieves a mAP of 94.4%, a 2% increase over the baseline model. Then, cattle keypoints are captured on RGB images and mapped to depth images, where keypoints are optimized using conditional filtering on the depth image. Finally, cattle dimension parameters are calculated using the cattle keypoints combined with Euclidean distance, the Moving Least Squares (MLS) method, Radial Basis Functions (RBFs), and Cubic B-Spline Interpolation (CB-SI). The average relative errors for the body height, lumbar height, body length, and chest girth of the 23 measured beef cattle were 1.28%, 3.02%, 6.47%, and 4.43%, respectively. The results show that the method proposed in this study has high accuracy and can provide a new approach to non-contact beef cattle dimension measurement. [ABSTRACT FROM AUTHOR]

Cryptosporidium parvum is a major zoonotic pathogen responsible for outbreaks of severe diarrhoea in humans and calves. Almost all investigations of cryptosporidiosis outbreaks caused by C. parvum have focused on its IIa subtype family in industrialized nations. From December 2018 to April 2019, approximately 200 neonatal calves on a large cattle farm in Hebei Province, China, were diagnosed with watery diarrhoea and over 40 died. To investigate the cause of the outbreak, faecal samples were taken during and after the outbreak from neonatal calves of ≤4 weeks of age (n = 40 and n = 56) and older calves of 4–24 weeks of age (n = 79 and n = 38). A total of 18 faecal samples collected from ill calves at the peak of the outbreak were analysed for four common enteric pathogens using an enzymatic immunoassay (EIA). In addition, 75 samples from neonatal calves were tested for rotavirus by EIA. All samples were analysed for Cryptosporidium spp. using PCR and sequencing techniques. Of the initial 18 samples from sick calves, ten were positive for C. parvum, five for rotavirus, and one for coronavirus. The overall prevalence of rotavirus in neonatal calves was 20.0% (15/75), with no significant differences during and after the outbreak. In contrast, Cryptosporidium parvum infections were significantly higher during the outbreak (60.0%, 24/40) than after the outbreak (30.4%, 17/56; p =.004). Cryptosporidium parvum infection was associated with the presence of watery diarrhoea in neonatal calves (OR = 11.19), while no association was observed between C. bovis infection and diarrhoea. All C. parvum isolates were identified as subtype IIdA20G1. This is one of the few reports of outbreaks of severe diarrhoea caused by C. parvum IId subtypes in calves. More attention should be directed towards the dissemination of C. parvum in China. [ABSTRACT FROM AUTHOR]

Supplementation with benzoic acid (BA) in animal feed can reduce feeds' acid-binding capacity, inhibit pathogenic bacterial growth, enhance nutrient digestion, and increase intestinal enzyme activities. This study aimed to investigate the effects of different doses of BA on the growth performance, rumen fermentation, and rumen microbiota of weaned Holstein dairy calves. Thirty-two Holstein calves at 60 days of age were randomly assigned into four groups (n = 8): a control group (fed with a basal diet without BA supplementation; CON group) and groups that were supplemented with 0.25% (LBA group), 0.50% (MBA group), and 0.75% (HBA group) BA to the basal diet (dry matter basis), respectively. The experiment lasted for 42 days, starting at 60 days of age and ending at 102 days of age, with weaning occurring at 67 days of age. Supplementation with BA linearly increased the average daily gain of the weaned dairy calves, which was significantly higher in the LBA, MBA, and HBA groups than that in the CON group. The average daily feed intake was quadratically increased with increasing BA supplementation, peaking in the MBA group. Supplementation with BA linearly decreased the feed-to-gain (F/G) ratio, but did not affect rumen fermentation parameters, except for the molar proportion of butyrate and iso-butyrate, which were linearly increased with the dose of BA supplementation. Compared with the CON group, the molar proportions of iso-butyrate in the LBA, MBA, and HBA groups and that of butyrate in the HBA group were significantly higher than those in the CON group. Supplementation with BA had no significant effect on the alpha and beta diversity of the rumen microbiota, but significantly increased the relative abundances of beneficial bacteria, such as Bifidobacterium , and reduced those of the harmful bacteria, such as unclassified_o__Gastranaerophilales and Oscillospiraceae_UCG-002 , in the rumen. Functional prediction analysis using the MetaCyc database revealed significant variations in the pathways associated with glycolysis across groups, including the GLYCOLYSIS-TCA-GLYOX-BYPASS, GLYCOL-GLYOXDEG-PWY, and P105-PWY pathways. In conclusion, BA supplementation improved the composition and function of rumen microbiota, elevated the production of butyrate and iso-butyrate, and increased the growth performance of weaned Holstein dairy calves.

Traditional measurement methods often rely on manual operations, which are not only inefficient but also cause stress to cattle, affecting animal welfare. Currently, non-contact cattle dimension measurement usually involves the use of multi-view images combined with point cloud or 3D reconstruction technologies, which are costly and less flexible in actual farming environments. To address this, this study proposes an automated cattle dimension measurement method based on an improved keypoint detection model combined with unilateral depth imaging. Firstly, YOLOv8-Pose is selected as the keypoint detection model and SimSPPF replaces the original SPPF to optimize spatial pyramid pooling, reducing computational complexity. The CARAFE architecture, which enhances upsampling content-aware capabilities, is introduced at the neck. The improved YOLOv8-pose achieves a mAP of 94.4%, a 2% increase over the baseline model. Then, cattle keypoints are captured on RGB images and mapped to depth images, where keypoints are optimized using conditional filtering on the depth image. Finally, cattle dimension parameters are calculated using the cattle keypoints combined with Euclidean distance, the Moving Least Squares (MLS) method, Radial Basis Functions (RBFs), and Cubic B-Spline Interpolation (CB-SI). The average relative errors for the body height, lumbar height, body length, and chest girth of the 23 measured beef cattle were 1.28%, 3.02%, 6.47%, and 4.43%, respectively. The results show that the method proposed in this study has high accuracy and can provide a new approach to non-contact beef cattle dimension measurement.