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Two body condition scoring systems were compared for assessing body condition of cows at the Scottish Agricultural College's Crichton Royal Farm. The weekly body condition scores (BCS) were collected for a period of 12 weeks (5 September-21 November). Scores were obtained using the primary systems utilized within the UK and USA. The USBCS were obtained by the same evaluator each week, while the UKBCS were obtained by two different evaluators alternating between weeks. Paired scores (n=2088) between the two systems within week were moderately correlated (r=0.75, P<0.0001). Regression equations to convert scores between the two systems were created using the GLM procedure of SAS (SAS Institute Inc., Cary NC, USA). The simple GLM models to convert from UK to US scores and US to UK scores were USBCS=1.182+0.816 * UKBCS (R²=0.56) and UKBCS=0.131+0.681 (R²=0.56), respectively. These equations may be used to interpret scores within the literature obtained using these two BCS systems, although they must be used with caution. [ABSTRACT FROM AUTHOR]

Time spent lying by lactating Holstein-Friesian cows of varying body condition scores (BCS) and milk yield was measured using an animal activity monitor. A 3-week average BCS was calculated for each cow; and in total, 84 cows were selected with 28 cows each among three BCS categories (Thin: BCS<2.75; Moderate: 2.75⩾BCS<3.25; Heavy: BCS⩾3.25) and two stage of lactation categories (<150 days in milk or >150 days in milk). Cows were kept in two management systems: parlour/freestall (n=60) or automated milking system/freestall (n=24). Behaviour was recorded for 5.3±0.1 d for each cow. Production levels were considered using a 28-d rolling average of daily milk production. Cows that exhibited clinical lameness before or during the observation period were excluded from analyses. For cows exhibiting oestrus, the day prior to, day of, and day following breeding were removed. The final analysis included 77 cows (408 d of observation). A mixed model was fitted to describe average daily hours spent lying. Results demonstrated that lying time increased as days in milk (DIM) increased (P=0.05). Variables that were tested but not significant (P>0.05) were BCS category, parity category (1 or ⩾2) and 28-d rolling average daily milk production. Although a numerical trend for increasing hours spent lying with increasing BCS was observed, after accounting for other factors in the mixed model, BCS did not significantly impact lying time. Continued investigation of these management factors that impact lying time and bouts, using new technologies, more cows, and more herds will help dairy owners better manage facilities and cow movements to optimize this essential behaviour. [ABSTRACT FROM AUTHOR]

Simple Summary: Precision technologies are often integrated on dairy farms to monitor individual animal health. One precision technology used is a bolus that is inserted into a cows' reticulorumen to monitor reticulorumen temperature. Although it is known that both water temperature and water quantity ingested are associated with changes in reticulorumen temperature, limited quantifiable research exists on the impact and magnitude of controlled water intake on reticulorumen temperature. We conducted an observational study to determine the effect of natural water ingestion on reticulorumen temperature, and performed a modified Latin square where cows were drenched with specific water quantities at specific temperatures to determine the effect of controlled water intake on reticuloruminal temperature. Our results demonstrated that water quantity and water temperature affect not only the magnitude of change in degrees in Celsius, but also how much time is required for reticulorumen temperature to return to baseline. This study provides insights in how to adjust the temperatures measures affected by water intake when using cattle reticulorumen temperature monitoring systems and how to estimate water consumption using decreases in reticulorumen temperature. Dairy precision technologies helps producers monitor individual animals. Reticulorumen temperature boluses are a way to monitor core body temperature; however, factors such as water intake affects reticulorumen temperature. This research determined the effect of natural water intake and a controlled water drench on reticulorumen temperature (RT) in dairy cattle. In observational study part 1, tie- stall cows (n = 4) with RT transponders were observed for natural water intake (recorded by in line water meters) for 48 h. In experiment part 2, a randomized Latin square design with cows (n = 12) restricted on feed for 4 h, were drenched daily with a water quantity of 6.7 L, 11.4 L or 22.7 L, and at controlled water temperature of 1.7 °C, 7.2 °C, 15.5 °C, or 29.4 °C. Descriptively, observational study 1 had (Mean ± SD 0.27 ± 0.31 L ingested per drinking event (n = 84) and RT decline from baseline was 2.29 ± 1.82 °C. For the experiment, a 48-h specific rolling baseline temperature range (BTR) was calculated for each cow prior to the experiment to determine time required for RT to reach BTR, and time to return to BTR. In part 2 of the experiment, as water quantity increased, RT had a greater maximum degree drop from baseline. Water temperature and water quantity interaction influenced time required for BTR to reestablish. The coldest water temperature at the highest drench quantity affected time for BTR to reestablish the longest (103 min). Results from this study suggest that an algorithm could be designed to predict water intake events for producers using reticulorumen temperature. [ABSTRACT FROM AUTHOR]

Body condition score (BCS) is an important management tool in the modern dairy industry, and one of the basic techniques for animal welfare and precision dairy farming. The objective of this study was to use a vision system to evaluate the fat cover on the back of cows and to automatically determine BCS. A 3D camera was used to capture the depth images of the back of cows twice a day as each cow passed beneath the camera. Through background subtraction, the back area of the cow was extracted from the depth image. The thurl, sacral ligament, hook bone, and pin bone were located via depth image analysis and evaluated by calculating their visibility and curvature, and those four anatomical features were used to measure fatness. A dataset containing 4820 depth images of cows with 7 BCS levels was built, among which 952 images were used as training data. Taking four anatomical features as input and BCS as output, decision tree learning, linear regression, and BP network were calibrated on the training dataset and tested on the entire dataset. On average, the BP network model scored each cow within 0.25 BCS points compared to their manual scores during the study period. The measured values of visibility and curvature used in this study have strong correlations with BCS and can be used to automatically assess BCS with high accuracy. This study demonstrates that the automatic body condition scoring system has the possibility of being more accurate than human scoring. [ABSTRACT FROM AUTHOR]