Employee perception of precision technology use at the dairy farm

The adoption of precision technologies on dairy farms has increased significantly in recent decades, leading to the challenge of providing employees with resources to maximize the efficient use of these tools. The objective of this study was to explore how dairy farm employees perceive the available precision technologies and to identify possible challenges they face when adapting to their use at the farm. An online survey consisting of four sections (employee demographics, precision technologies in use, perception of these technologies, and opportunities for adapting to technology use) was completed from September to December 2022 by 266 farm employees from three dairies operated under similar management. Most of the respondents were identified as male (72.2%), Hispanic or Latino (92.5%), aged between 21 and 30 (39.1%) or 31 and 40 yr (36.8%), with a bachelor's degree (34.6%) or completion of middle school (29.3%) and having basic or no English proficiency (74%). Overall, the respondents indicated being comfortable (95.6%) with and understanding (91.8%) the technology they use. Employees recognized precision technology as a tool that helps them to be more efficient (93.7%), identifying the technologies' benefits (92.1%). However, challenges for adapting to these technologies included personal limitations, such as not knowing the language of the technology (31%), visual impairments (24%), light sensitivity (14%), and not being able to read (7%). Environmental limitations were also recognized and included cold weather (64.3%), wind (46%), and surroundings that were too dark (31%) or too bright (21%). Significant associations between perception of the technology and age, level of education, and English proficiency were identified. Respondents indicated their desire to learn more about precision technologies implemented at work, which could eventually lead to improved efficiency at the dairy operation through innovations in the way users interact with these technologies, increasing employees' motivation. This study provides insights that could assist the dairy industry in addressing challenges and enhancing opportunities for a more efficient use of precision technologies at dairy farms.

Evaluation of reticuloruminal temperature for the prediction of clinical mastitis in dairy cows challenged with Streptococcus uberis

Automated monitoring devices have become increasingly utilized in the dairy industry, especially for monitoring or predicting disease status. While multiple automated monitoring devices have been developed for the prediction of clinical mastitis (CM), limitations in performance or applicability remain. The aims of this study were to (1) detect variations in reticuloruminal temperature (RRT) relative to an experimental intramammary challenge with Streptococcus uberis and (2) evaluate alerts generated automatically based on variation in RRT to predict initial signs of CM in the challenged cows based on severity of clinical signs and the concentration of bacteria (cfu/mL) in the infected quarter separately. Clinically healthy Holstein cows without a history of CM in the 60 d before the experiment (n = 37, parity 1 to 5, ≥120 d in milk) were included if they were microbiologically negative and had a somatic cell count under 200,000 cells/mL based on screening of quarter milk samples 1 wk before challenge. Each cow received an intra-reticuloruminal automated monitoring device before the trial and was challenged with 2,000 cfu of Strep. uberis 0140J in 1 rear quarter. Based on interrupted time series analysis, intramammary challenge with Strep. uberis increased RRT by 0.54°C [95% confidence interval (CI): 0.41, 0.66] at 24 h after the challenge, which remained elevated until the end of the study. Alerts based on RRT correctly classified 78.3% (95% CI: 65.8, 87.9) of first occurrences of CM at least 24 h in advance, with a sensitivity of 70.0% (95% CI: 50.6, 85.3) and a specificity of 86.7% (95% CI: 69.3, 96.2). The accuracy of CM for a given severity score was 90.9% (95% CI: 70.8, 98.9) for mild cases, 85.2% (95% CI: 72.9, 93.4) for moderate cases, and 92.9% (95% CI: 66.1, 99.8) for severe cases. Test characteristics of the RRT alerts to predict initial signs of CM improved substantially after bacterial count in the challenged quarter reached 5.0 log₁₀ cfu/mL, reaching a sensitivity of 73.5% (95% CI: 55.6, 87.1) and a specificity of 87.5% (95% CI: 71.0, 96.5). Overall, the results of this study indicated that RRT was affected by the intramammary challenge with Strep. uberis and the RRT-generated alerts had similar accuracy as reported for other sensors and algorithms. Further research that includes natural infections with other pathogens as well as different variations in RRT to determine CM status is warranted.

Short communication: Evaluation of an eating time sensor for use in pasture-based dairy systems

The assessment of grazing behavior is important for research and practice in pasture-grazed dairy farm systems. However, few devices are available that enable assessment of cow grazing behavior at an individual animal level. This study investigated whether commercially available Smarttag "eating time" sensors (Nedap Livestock Management, Groenlo, the Netherlands) were suitable for recording the grazing time of cows. Smarttag sensors were mounted on the neck collars of multiparous Holstein-Friesian cows in a herd in Taranaki, New Zealand. Cows were randomly selected each observation day from the milking herd for 8 separate days across a 1-mo period. Trained observers conducted 90-min observation periods to evaluate the relationship between the sensor eating time measure and grazing time. A set of 5 defined cow behaviors (2 "head up" and 3 "head down" behaviors) were assessed. In total, observations of 37 cows were recorded in 14 sessions over 8 d in the study period, providing 55.5 total hours of observations. Observation data were aligned with sensor data according to the sensor time stamps and grouped into matching 15-min intervals. Interobserver reliability was assessed both before and after the main trial period, and the mean percentage eating time per observer had a coefficient of variation of 0.46% [mean 93.2, standard deviation (SD) 0.425] before and 0.07% (mean 96.3, SD 0.074) after. In the main trial, the relationship between observed (mean 70.8%) and sensor-derived (mean 69.3%) percentage eating time over the observation period gave a Pearson correlation coefficient of 0.971, concordance correlation coefficient 0.968, mean difference 1.50% points, and SD 5.8% points. Therefore, sensor-identified percentage "eating time" and observed percentage active grazing time were shown to be both very well correlated and concordant (in agreement, with high correlation and little bias). Therefore, the relationship between observed and sensor-derived data had a high degree of agreement for identifying cow grazing activity. In conclusion, Smarttag sensors are a valid and useful tool for estimating grazing activity at time periods of 1 h or more.

Individual characteristics in early life relate to variability in weaning age, feeding behavior, and weight gain of dairy calves automatically weaned based on solid feed intake

Little is known about factors affecting individual variability in weaning age, feeding behavior, and growth of dairy calves. The objectives of this study were to (1) describe early-life individual characteristics of dairy calves and how these relate to weaning age, feeding behavior, and performance during the first 15 wk of age, and (2) to identify which of these individual characteristics predict weaning age of calves automatically weaned based on solid feed intake. Early-life characteristics of calves (n = 43) included scores for vitality at birth, drinking ability, learning ability to use the automated milk feeder in a group pen from d 1 of age, and personality traits assessed using exposure to a novel environment, a human, and an object at d 21 of age. Calves received 12 L/d of milk until d 30 when milk was reduced by 25% relative to the individual's previous 3-d intake average. Calves were weaned based on intake of solid feed (milk reduced by 25% at each of 2 intermediate solid feed intake targets, 225 and 675 g/d), and were weaned when they consumed 1,300 g/d of solid feed, resulting in variable weaning ages. A principal component analysis identified 5 factors that we labeled as low vitality, fearful, strong drinker, slow learner, and exploratory-active. Calves that were slow learners weaned at a later age, whereas fearful calves weaned earlier. No other early-life individual characteristics were associated with weaning age. Other characteristics (low vitality, strong drinkers, and exploratory-active) were associated with some measures of feeding behavior, feed intake, and growth, especially during the preweaning period. Measures of early solid feed intake (age to start eating and total preweaning intake) were best able to predict weaning age of calves. Individual early-life characteristics and measures of early solid feed intake can identify calves likely to do well or struggle during weaning.