Farm management and economic factors associated with bulk tank total bacterial count in Holstein dairy herds in Iran.

The objectives of this research were (1) to study different factors affecting milk total bacterial count (TBC) and (2) to estimate the economic value associated with TBC in Holstein dairy herds in Iran. The relationships between bulk tank TBC and farm management and economic factors were examined on 56 randomly selected intensive dairy farms. Herd management factors associated with bulk tank TBC were determined using mixed linear models. The median bulk tank TBC for the sample herds was 299 (range 81-1185) × 103 cfu/ml. The average economic premium opportunity from bulk tank TBC was US$ 1.32 per ton of milk ranging from US$ 0.02 per ton of milk for herds applying wet tissue procedures as teat cleaning material and washing the water troughs three times per day to US$ 5.20 per ton of milk for herds with dirty barns. Results showed that the following management factors were associated with low TBC and high economic value: frequency of cleaning water troughs, teat cleaning material, the frequency of milk delivery to the processor, bedding material, herd size, education level of workers, udder washing material, material of milking parlor wall, frequency of disinfection of the calving area, presence of veterinarian, water quality control, having a hospital pen and barn hygiene. In conclusion, our findings highlight the need to pay more attention to farm management issues, particularly farm hygiene practices to reduce milk TBC and so reduce the economic burden of TBC in dairy herds in Iran.

Validation of a Commercial Automated Body Condition Scoring System on a Commercial Dairy Farm.

Body condition scoring (BCS) is the management practice of assessing body reserves of individual animals by visual or tactile estimation of subcutaneous fat and muscle. Both high and low BCS can negatively impact milk production, disease, and reproduction. Visual or tactile estimation of subcutaneous fat reserves in dairy cattle relies on their body shape or thickness of fat layers and muscle on key areas of the body. Although manual BCS has proven beneficial, consistent qualitative scoring can be difficult to implement. The desirable BCS range for dairy cows varies within lactation and should be monitored at multiple time points throughout lactation for the most impact, a practice that can be hard to implement. However, a commercial automatic BCS camera is currently available for dairy cattle (DeLaval Body Condition Scoring, BCS DeLaval International AB, Tumba, Sweden). The objective of this study was to validate the implementation of an automated BCS system in a commercial setting and compare agreement of the automated body condition scores with conventional manual scoring. The study was conducted on a commercial farm in Indiana, USA, in April 2017. Three trained staff members scored 343 cows manually using a 1 to 5 BCS scale, with 0.25 increments. Pearson's correlations (0.85, scorer 1 vs. 2; 0.87, scorer 2 vs. 3; and 0.86, scorer 1 vs. 3) and Cohen's Kappa coefficients (0.62, scorer 1 vs. 2; 0.66, scorer 2 vs. 3; and 0.66, scorer 1 vs. 3) were calculated to assess interobserver reliability, with the correlations being 0.85, 0.87, and 0.86. The automated camera BCS scores were compared with the averaged manual scores. The mean BCS were 3.39 ± 0.32 and 3.27 ± 0.27 (mean ± SD) for manual and automatic camera scores, respectively. We found that the automated body condition scoring technology was strongly correlated with the manual scores, with a correlation of 0.78. The automated BCS camera system accuracy was equivalent to manual scoring, with a mean error of -0.1 BCS and within the acceptable manual error threshold of 0.25 BCS between BCS (3.00 to 3.75) but was less accurate for cows with high (>3.75) or low (<3.00) BCS scores compared to manual scorers. A Bland-Altman plot was constructed which demonstrated a bias in the high and low automated BCS scoring. The initial findings show that the BCS camera system provides accurate BCS between 3.00 to 3.75 but tends to be inaccurate at determining the magnitude of low and high BCS scores. However, the results are promising, as an automated system may encourage more producers to adopt BCS into their practices to detect early signs of BCS change for individual cattle. Future algorithm and software development is likely to increase the accuracy in automated BCS scoring.

Impact of Observed and Controlled Water Intake on Reticulorumen Temperature in Lactating Dairy Cattle.

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.

Comparison of two methods of assessing dairy cow body condition score.

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.

Influence of milk yield, stage of lactation, and body condition on dairy cattle lying behaviour measured using an automated activity monitoring sensor.

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 > or = BCS<3.25; Heavy: BCS> or = 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.