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.

Weighting the relative importance of behaviors affecting gait score.

This research communication evaluates experts' opinions on the importance and weights of six gait aspects. In 2016, a Qualtrics (Qualtrics LLC., Provo, Utah) survey was distributed to lameness experts. Six gait aspects - general symmetry, tracking, spine curvature, head bobbing, speed and abduction as well as adduction were included. Respondents were asked to rank the gait aspects from 1 (most important) to 6 (least important), and to indicate which weight each gait aspect should receive when assessing lameness. For each gait aspect, frequency (percentage %) was used to describe the distribution of rank, and medians as well as 25th and 75th percentiles were used to summarize assigned weights. Thirty-nine percent of respondents ranked general symmetry first, followed by 32% for tracking, and 19% ranked spine curvature third. Head bobbing ranked fourth with 10% whereas, speed, abduction and adduction were not ranked. The median, 25th and 75th percentiles weight for each gait aspect were: general symmetry (25, 15, and 30), tracking (20, 10, and 30), spine curvature (20, 10, and 21), head bobbing (15, 10, and 20), speed (10, 5, and 20), and abduction and adduction (10, 5, and 10). General symmetry and tracking were deemed the most important gait aspects. A composite gait score can be calculated based on weighted importance of different gait aspects to indicate possible lameness.

Dairy producers in the Southeast United States are concerned with cow care and welfare.

This research communication addresses the hypothesis that Southeast dairy producers' self-reported bulk tank somatic cell count (BTSCC) was associated with producers' response to three statements (1) 'a troublesome thing about mastitis is the worries it causes me,' (2) 'a troublesome thing about mastitis is that cows suffer,' and (3) 'my broad goals include taking good care of my cows and heifers.' Surveys were mailed to producers in Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, and Virginia (29% response rate, N = 596; final analysis N = 574), as part of a larger survey to assess Southeastern dairy producers' opinions related to BTSCC. Surveys contained 34 binomial (n = 9), Likert scale (n = 7), and descriptive (n = 18) statements targeted at producer self-assessment of herd records, management practices, and BTSCC. Statements 1 and 2 were assessed on a 5-point Likert scale from 'strongly disagree' to 'strongly agree.' Statement 3 was assessed on a 5-point Likert scale from 'very unimportant' to 'very important.' Reported mean BTSCC for all participants was 254 500 cells/ml. Separate univariable logistic regressions using generalized linear mixed models (SAS 9.4, Cary, NC, USA) with a random effect of farm, were performed to determine if BTSCC was associated with probability for a producer's response to statements. If BTSCC was significant, forward manual addition was performed until no additional variables were significant (P ≤ 0.05), but included BTSCC, regardless of significance. Bulk tank somatic cell count was associated with 'a troublesome thing about mastitis is the worries it causes me,' but not with Statements 2 or 3. This demonstrates that >75% of Southeastern dairy producers are concerned with animal care and cow suffering, regardless of BTSCC. Understanding Southeast producers' emphasis on cow care is necessary to create targeted management tools for herds with elevated BTSCC.

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.

Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production.

Consumption of animal products such as meat, milk, and eggs in first-world countries has leveled off, but it is rising precipitously in developing countries. Agriculture will have to increase its output to meet demand, opening the door to increased automation and technological innovation; intensified, sustainable farming; and precision livestock farming (PLF) applications. Early indicators of medical problems, which use sensors to alert cattle farmers early concerning individual animals that need special care, are proliferating. Wearable technologies dominate the market. In less-value-per-animal systems like sheep, goat, pig, poultry, and fish, one sensor, like a camera or robot per herd/flock/school, rather than one sensor per animal, will become common. PLF sensors generate huge amounts of data, and many actors benefit from PLF data. No standards currently exist for sharing sensor-generated data, limiting the use of commercial sensors. Technologies providing accurate data can enhance a well-managed farm. Development of methods to turn the data into actionable solutions is critical.

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.