Lykkegaardpilgaard5728

teres. The effect of 3-NOP on colonizing methanogenic microbes differed depending upon the forage incubated, as abundance of genus Methanobrevibacter was decreased for barley silage but not for grass hay. In conclusion, 3-NOP supplementation of a high-forage diet decreased ruminal methanogenesis and increased dissolved H2 concentration, but had no negative effects on ruminal fiber degradation and only minor effects on relative abundances of the major taxa of bacteria adhered to forage substrates incubated in the rumen.The objective of this study was to longitudinally quantify Escherichia coli resistant to ciprofloxacin and ceftriaxone in calves treated with enrofloxacin or tulathromycin for the control of bovine respiratory disease (BRD). Dairy calves 2 to 3 wk of age not presenting clinical signs of pneumonia and at high risk of developing BRD were randomly enrolled in 1 of 3 groups receiving the following treatments (1) single label dose of enrofloxacin (ENR); (2) single label dose of tulathromycin (TUL); or (3) no antimicrobial treatment (control, CTL). Fecal samples were collected immediately before administration of treatment and at d 2, 4, 7, 14, 21, 28, 56, and 112 d after beginning treatment. Samples were used for qualification of E. coli using a selective hydrophobic grid membrane filter (HGMF) master grid. The ENR group had a significantly higher proportion of E. Gefitinib supplier coli resistant to ciprofloxacin compared with CTL and TUL at time points 2, 4, and 7. At time point 28, a significantly higher proportion of E. coli resistant to ciprofloxacin was observed only compared with CTL. The TUL group had a significantly higher proportion of E. coli resistant to ciprofloxacin compared with CTL at time points 2, 4, and 7. None of the treatment groups resulted in a significantly higher proportion of E. coli isolates resistant to ceftriaxone. Our study identified that treatment of calves at high risk of developing BRB with either enrofloxacin or tulathromycin resulted in a consistently higher proportion of ciprofloxacin-resistant E. coli in fecal samples.Hypocalcemia is a common metabolic disorder of transition dairy cows that is considered a gateway disease, increasing the risk of other health disorders and reducing cow performance. Clinical milk fever is associated with long periods of recumbency, and it is plausible that cows experiencing non-paretic hypocalcemia may spend more time lying; hence, lying behavior and activity measures may be useful in identifying at-risk cows. The objective of this study was to describe associations among blood calcium (Ca) status at calving and lying behavior and activity measures during the transition period in grazing dairy cows. Blood was sampled on the day of calving (d 0), and d 1, 2, 3, and 4 postcalving, and analyzed for total plasma Ca concentration. Twenty-four multiparous Holstein-Friesian and Holstein-Friesian × Jersey grazing dairy cows were classified, retrospectively, as clinically hypocalcemic (CLIN; blood Ca ≤ 1.4 mmol/L at 1 or more consecutive samplings within 48 h postcalving, but without parturient paresnces in behavior were no longer detected 2 d postcalving, and no further differences were observed. The day before calving, the CLIN group spent 1.4 h longer lying down than did the SUB and NORM groups. Further, the relative change in steps from a precalving baseline period (d -14 to -7) until d 0 was positively, linearly associated with blood Ca concentration within 24 h postcalving. Future work should consider daily and temporal changes in behavior in individual cows to determine the potential for these measures to allow early detection of hypocalcemia.Because infections with pathogenic bacteria entering the mammary gland through the teat canal are the most common cause of mastitis in dairy cows, sustaining the integrity of the teat canal and its adjacent tissues is critical to resist infection. The ability to monitor teat tissue condition is therefore a key prerequisite for udder health management in dairy cows. However, to date, routine assessment of teat-end condition is limited to cow-side visual inspection, making the evaluation a time-consuming and expensive process. Here, we illustrate and demonstrate a method for assessing teat-end condition of dairy cows through digital images and software. A digital workflow has been designed where images of dairy cow teats are obtained and processed to display individual teats, and the cow and teat images are labeled and displayed through a graphical user interface. The interface then allows an evaluator to assess quarter- and cow-level teat-end condition and store the results for review and future analysis. The ence interval 0.53-0.79) was observed when using cow-side assessments at the cow level. This suggests that image-based teat-end condition classification is possible, and coupled with improvements in image acquisition and image processing, this method can be used to assess teat-end condition in a systematic and convenient manner.The housing types (HST) in which dairy cows are kept and the feeding systems (FDS) used differ among farmers in Japan. Here, we investigated the genetic relationships among conception rate at first insemination (CR) and milk production traits (PROD) during the first 3 lactations of Holstein cows by using a multiple-trait model that considered the trait values of herds with different HST [tiestall (TSL) barn, freestall (FS) barn, or grazing (GZ)] and FDS as separate traits. Milk production and conception records of Holstein cows in the Hokkaido region of Japan (283,611 records for first lactation, 253,902 for second, and 181,197 for third) were analyzed. We categorized herds with TSL or FS into 2 types of FDS for cows separate feeding (SF) of roughage plus concentrate or feeding of total mixed ration, in which roughage and concentrates were mixed before feeding. The PROD analyzed were cumulative milk, fat, and protein yields within 305 d and lactation persistency, which we defined as the difference between milgenetic relationships between fertility traits and milk production traits.Digital dermatitis (DD) is linked to severe lameness, infertility, and decreased milk production in cattle. Early detection of DD provides an improved prognosis for treatment and recovery; however, this is extremely challenging on commercial dairy farms. Computer vision (COMV) models can help facilitate early DD detection on commercial dairy farms. The aim of this study was to develop and implement a novel COMV tool to identify DD lesions on a commercial dairy farm. Using a database of more than 3,500 DD lesion images, a model was trained using the YOLOv2 architecture to detect the M-stages of DD. The YOLOv2 COMV model detected DD with an accuracy of 71%, and the agreement was quantified as "moderate" by Cohen's kappa when compared with a human evaluator for the internal validation. In the external validation, the YOLOv2 COMV model detected DD with an accuracy of 88% and agreement was quantified as "fair" by Cohen's kappa. Implementation of COMV tools for DD detection provides an opportunity to identify cows for DD treatment, which has the potential to lower DD prevalence and improve animal welfare on commercial dairy farms.