Effect of nonsteroidal anti-inflammatory drugs on neonatal calf diarrhea when administered at a disease alert generated by automated milk feeders

The objective of this randomized clinical trial was to assess whether early intervention with a nonsteroidal anti-inflammatory drug (NSAID) following a disease alert generated by automated milk feeders could reduce diarrhea severity and improve performance in dairy calves. A total of 71 Holstein calves were enrolled on an automated milk feeder (recorded milk intake and drinking speed) at 3 d of age and received up to 15 L/d (150 g/L) of milk replacer until 35 d of age. An alert that was previously validated as diagnostically accurate to identify calves at risk for diarrhea was used using automated milk feeder data (≤60% rolling dividends in milk intake or drinking speed over 2 d). At their first alert, calves were randomly allocated to receive a single subcutaneous injection of meloxicam (Metacam, Boehringer Ingelheim) at a rate of 0.5 mg/kg of BW (NSAID) or an equal volume of saline as a placebo control (CON). Fecal consistency was scored daily, and calves were diagnosed with diarrhea when they had loose feces for ≥2 d or watery feces for ≥1 d. Body weight was recorded at birth and weekly thereafter. A subset of calves (n = 32) were fitted with IceQube pedometers at 3 d of age to measure activity behaviors (lying time and step count). Mixed linear regression models were used to assess the association of study treatment with the duration of diarrhea after the alert and to evaluate the association of study treatment with milk intake, drinking speed, lying time, overall activity for 5 d following the alert, and ADG for 3 wk following the alert. On average, calves triggered an alert at (mean ± SD) 9.3 ± 2.3 d of age and were diagnosed with diarrhea at 9.6 ± 2.1 d of age. Diarrhea duration was similar between treatments (NSAID: 2.85 vs. CON: 2.94 ± 0.37 d), as were feeding behaviors (milk intake [NSAID: 8.2 vs. CON: 8.1 ± 0.4 L/d] and drinking speed [NSAID: 0.38 vs. CON: 0.37 ± 0.02 min/L]). Treatment was also not associated with ADG for the 3 wk after the alert (NSAID: 0.97 vs. CON: 0.97 ± 0.06 kg/d). However, calves provided an NSAID had reduced odds of being treated with electrolytes (odds ratio = 0.32, 95% CI: 0.10-0.98). In addition, calves provided an NSAID spent less time lying (NSAID: 17.64 vs. CON: 18.17 ± 0.19 h/day) and performed more steps over the 5 d following the alert (NSAID: 789.1 vs. CON: 628.0 steps/d), suggesting that CON calves may have been more lethargic. Overall, providing an NSAID at the time of a diarrhea alert did not affect diarrhea duration, feed intake, or growth. However, providing an NSAID increased activity in the 5 d following the alert, which may have reduced pain and symptoms of lethargy, indicating a milder response to the disease. We suggest that providing an NSAID at the time of diarrhea alert had little benefit on the calf; however, further work is needed to understand behaviors associated with malaise and pain in calves with diarrhea as well as the efficacy of NSAID under different management conditions.

The association of lung consolidation and respiratory pathogens identified at weaning on the growth performance of beef on dairy calves

This observational study evaluated the relationship between lung consolidation observed at weaning and calf ADG, and the association of pathogen shedding at weaning on ADG in beef × dairy calves up to 238 d. Beef × Holstein calves (n = 143) were sourced from 2 dairies. Calves were managed in 3 cohorts and fed milk replacer and calf starter preweaning. Calves were transported to another facility after weaning and raised in one group where they were fed calf starter with oat hay and transitioned to a corn silage-based total mixed ration diet. Calf ADG was calculated from arrival to weaning at 61 ± 14 d (period 1), from weaning to 83 ± 21 d (period 2), and from 83 d to 238 ± 21 d (period 3). Thoracic ultrasonography (TUS) was performed at weaning to evaluate if a calf had lung consolidation (characterized as TUS+ ≥ 1 cm2 in one lobe) and to categorize the degree of lung consolidation found (LC = none, 1 to 2 cm2, or 3 cm2). Nasopharyngeal swabs were taken from the TUS+ calves and from pair-matched TUS- calves (n = 35 pairs) for pathogen identification by culture at a diagnostic laboratory. A mixed linear regression model assessed the association of LC with calf ADG with LC, period, period × LC, and sire breed as fixed effects, arrival weight as a covariate, and calf nested within the cohort as a random effect. Another mixed linear regression model assessed the association of pathogen shedding with calf ADG from weaning to 238 d with period, and sire breed as fixed effects, pair was nested within cohort as a random effect. A logistic regression model was used to evaluate the likelihood of a TUS+ calves shedding a pathogen with pair as a fixed effect. There was an LC × period interaction affecting ADG over period 2 (P = 0.03) where TUS- calves had increased ADG (1.18 ± 0.02 kg/d) compared with calves with LC = 3 cm2 (1.03 ± 0.04 kg/d, P = 0.01). However, TUS- calves had similar ADG to calves with LC = 1 to 2 cm2 in period 2 (P > 0.05). Calf ADG was not associated with LC in period 3 (P > 0.05), and calves weighed 324 ± 37 kg (mean ± SD) at 238 d. In addition, 57% (20/35) of TUS+ calves and 26% (9/35) TUS- calves shed Pasteurella multocida. There was no association of pathogen shedding with calf ADG (P > 0.05), but TUS+ calves were more likely to shed a pathogen. These findings suggest that calves with pneumonia experienced poor growth up to 20 d post-weaning, but compensatory gain occurred by 238 d. Furthermore, P. multocida was not associated with growth performance up to 238 d in beef × dairy calves.

Are you ready for a challenge? Personality traits influence dairy calves' responses to disease, pain, and nutritional challenges

Dairy calves routinely experience disease, pain, and nutritional stressors such as diarrhea, dehorning, and weaning early in life. These stressors lead to changes in behavioral expression that varies in magnitude between individuals, where a greater magnitude change would suggest lower resilience in individuals to a stressor. Thus, this study first aimed to quantify the individual variation in magnitude change in feeding behaviors and activity in response to a bout of diarrhea, dehorning, and weaning. The next objective was to then investigate if personality traits were related to this magnitude of behavioral response in dairy calves, and thus their resilience toward these stressors. Calves were followed with 2 precision livestock technologies (e.g., an automatic feeding system, and leg accelerometer) to track behavioral changes in response during the time when the stressors were present. The automatic feeding system provided daily measures of milk intake, drinking speed, rewarded and unrewarded visits to the milk feeding station, and calf starter intake. The leg accelerometer provided daily measures of steps, activity index, lying time, and lying bouts. At 23 ± 3 d of age, Holstein dairy calves (n = 49) were subjected to a series of standardized personality tests that exposed the calf to novelty and fear stimuli. Factors extracted from a principal component analysis on the behaviors from the personality test were interpreted as personality traits: Factor 1 (fearful), Factor 2 (active) and Factor 3 (explorative). The magnitude changes in behaviors from the precision livestock technologies were calculated relative to the behavior performed on the day the stressor occurred (i.e., day of diagnosis, day of dehorning, day weaned). Linear regression models were used to determine whether calf scores on each factor were associated with magnitude change in behavior for each of the stressor periods with day relative to the stressor included as a repeated measure. Models were run independently for the period leading up to and following each stressor. We found that calves varied in their behavioral responses to diarrhea, dehorning, and weaning stressors, despite being reared in the same environment and experiencing consistent management procedures. Additionally, personality traits measured from standardized tests were associated with both the direction and magnitude of change in behaviors around each stressor. For instance, with diarrhea, calves that were highly fearful had a greater magnitude change in milk intake and drinking speed following diagnosis than the least fearful calves. With dehorning, calves that were highly explorative had a greater magnitude change in lying time when dehorned, but a smaller magnitude change in lying bouts and drinking speed following dehorning, compared with the least explorative calves. With weaning, calves that were highly active had a smaller magnitude change in unrewarded visits leading up to and following weaning than calves that were the least active. Each of the personality traits had a significant association with change in behavior surrounding each of the stressors evaluated, although these associations depended on the type of stressor. These results have implications for how individual calves experience each stressor and therefore individual animal welfare.

Developing a tool to assess the health-related quality of life in calves with respiratory disease: tool refinement and construct validity testing

Bovine respiratory disease (BRD) is a major source of morbidity and mortality in calves and detection of the disease can be challenging. Diagnostic tools for BRD are typically based on the assessment of clinical signs. As experience of disease is associated with a poor quality of life and this poor emotional experience can be expressed in observable behaviour patterns, a quality-of-life approach might identify new indicators of disease. Health-related quality of life (HRQOL) approaches are widely used in human medicine but are rarely used in livestock. This study aimed to refine and validate an HRQOL tool for calves with BRD that had been created in a previous study. The tool contained 13 items/indicators across two domains (clinical signs and behavioural expression). One hundred preweaned dairy-bred calves were scored daily using the HRQOL tool and also using the industry-recognised Wisconsin health score as a 'gold standard'. The score assigned to each of the 13 items of the HRQOL tool was summed to give an accumulated HRQOL score for each calf/day. To refine the tool, the items within the tool were compared to each other to identify high levels of correlation or redundancy. This resulted in the retention of three items within the clinical signs domain (body and head posture, respiratory effort and ear carriage) and four within the behavioural expression domain (vigour, movement to feed, motivation at feed and volume of feed consumed). To determine whether the refined HRQOL tool could successfully differentiate sick from healthy animals, the Wisconsin score was used to create two matched groups of 28 'sick' and 28 'healthy' calves. There was a significant difference in the HRQOL scores between the two groups (H = 14.09, df = 1, P = 0.000) suggesting that the new tool could differentiate between 'healthy' and 'sick' calves. In terms of the two domains, there was a significant difference (P < 0.001) between the 'healthy' and 'sick' calves for the overall HRQOL tool score for the clinical signs domain, but there was no significant difference (P = 0.154) between 'healthy' and 'sick' calves in the overall HRQOL score for the behavioural expression domain; however, the combined tool was the most accurate. Overall, this study has demonstrated that the new HRQOL tool can differentiate between sick and healthy calves. Some or all of the indicators could be used alongside existing disease-detection tools for preweaned calves. However, future work would be needed to validate this HRQOL tool in other production systems.

Graduate Student Literature Review: Social and feeding behavior of group-housed dairy calves in automated milk feeding systems

Automated milk feeders (AMF) allow farmers to raise calves in groups while generating individual records on milk consumption, drinking speed, and frequency of visits. Calves raised in groups benefit from social interaction, which facilitates learning and adapting to novelty. However, calves in large groups (>12 calves/feeder) experience a higher risk of disease transmission and competition than those housed individually or in smaller groups. Therefore, if group size, grouping strategy, and disease detection are not optimal, the health and performance of calves can be compromised. The objectives of this narrative literature review, from publications available as of February 2023, are to (1) describe the use of AMF in group housing systems for calves and the associated feeding behavior variables they automatically collect, (2) linking feeding behavior collected from AMF to disease risk in calves, (3) describe research on social behavior in AMF systems, and (4) introduce social networks as a promising tool for the study of social behavior and disease transmission in group-housed AMF-fed calves. Existing research suggests that feeding behavior measures from AMF can assist in detecting bovine respiratory disease and enteric disease, which are common causes of morbidity and mortality for preweaning dairy heifers. Automated milk feeder records show reduced milk intake, drinking speed, or frequency of visits when calves are sick. However, discrepancies exist among published research about the sensitivity of feeding behavior measures as indicators of sickness, likely due to differences in feeding plans and disease-detection protocols. Therefore, considering the influence of milk allowance, group density, and individual variation on the analysis of AMF data is essential to derive meaningful information used to inform management decisions. Research using dynamic social networks derived from precision data show potential for the use of social network analysis to understand disease transmission and the effect of disease on social behavior of group-housed calves.

Association of Bovine Respiratory Disease during the Pre-Weaning Period with Blood Cell Counts and Circulating Concentration of Metabolites, Minerals, and Acute Phase Proteins in Dairy Calves Transported to a Calf Raising Facility

Our objective was to investigate the association of bovine respiratory disease (BRD) occurring within the first 56 days of life with blood cell counts and the circulating concentration of metabolites, minerals, and acute phase proteins throughout the pre-weaning period in dairy calves transported to a heifer raising facility within their first week of life. Data from 305 calves transported from dairies in Minnesota to a calf raising facility in New Mexico within their first four days of life were used in this retrospective cohort study. Blood samples were collected at 7, 17, 34, and 56 days of life for the analysis of blood cell counts, biochemistry, and the concentration of acute phase proteins. Blood urea nitrogen, albumin, GLDH, CK, P, Na, K, Cl, Zn, Hp, SAA, and monocyte counts were associated with BRD status throughout or at least at one of the time points evaluated in this study. In conclusion, several hematological variables were associated with BRD status in dairy calves that underwent transportation stress in early life.

Early detection of bovine respiratory disease in pre-weaned dairy calves using sensor based feeding, movement, and social behavioural data

Previous research shows that feeding and activity behaviours in combination with machine learning algorithms has the potential to predict the onset of bovine respiratory disease (BRD). This study used 229 novel and previously researched feeding, movement, and social behavioural features with machine learning classification algorithms to predict BRD events in pre-weaned calves. Data for 172 group housed calves were collected using automatic milk feeding machines and ultrawideband location sensors. Health assessments were carried out twice weekly using a modified Wisconsin scoring system and calves were classified as sick if they had a Wisconsin score of five or above and/or a rectal temperature of 39.5 °C or higher. A gradient boosting machine classification algorithm produced moderate to high performance: accuracy (0.773), precision (0.776), sensitivity (0.625), specificity (0.872), and F1-score (0.689). The most important 30 features were 40% feeding, 50% movement, and 10% social behavioural features. Movement behaviours, specifically the distance walked per day, were most important for model prediction, whereas feeding and social features aided in the model's prediction minimally. These results highlighting the predictive potential in this area but the need for further improvement before behavioural changes can be used to reliably predict the onset of BRD in pre-weaned calves.

Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves

Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from automatic milk feeding machines to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 preweaning female Holstein calves were collected daily over a period of 6 yr (3 yr included per-visit data), and daily milk consumption (DMC), per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit, daily number of rewarded visits (DNRV), and total number of visits per day were recorded over a 60-d preweaning period. Additional traits were derived from these variables, including total consumption and duration variance (TCV and TDV), feeding interval, drinking speed (DS), and preweaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). The NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step genomic BLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 SNP after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models (0.006 ± 0.0009 to 0.08 ± 0.004). However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d preweaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of -0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.

Strategies for Bovine Respiratory Disease (BRD) Diagnosis and Prognosis: A Comprehensive Overview

Despite significant advances in vaccination strategies and antibiotic therapy, bovine respiratory disease (BRD) continues to be the leading disease affecting the global cattle industry. The etiology of BRD is complex, often involving multiple microbial agents, which lead to intricate interactions between the host immune system and pathogens during various beef production stages. These interactions present environmental, social, and geographical challenges. Accurate diagnosis is essential for effective disease management. Nevertheless, correct identification of BRD cases remains a daunting challenge for animal health technicians in feedlots. In response to current regulations, there is a growing interest in refining clinical diagnoses of BRD to curb the overuse of antimicrobials. This shift marks a pivotal first step toward establishing a structured diagnostic framework for this disease. This review article provides an update on recent developments and future perspectives in clinical diagnostics and prognostic techniques for BRD, assessing their benefits and limitations. The methods discussed include the evaluation of clinical signs and animal behavior, biomarker analysis, molecular diagnostics, ultrasound imaging, and prognostic modeling. While some techniques show promise as standalone diagnostics, it is likely that a multifaceted approach-leveraging a combination of these methods-will yield the most accurate diagnosis of BRD.

Effect of once-a-day milk feeding on behavior and growth performance of pre-weaning calves

OBJECTIVE

The objectives of the present study were to evaluate the effects of once-a-day milk feeding on growth performance and routine behavior of preweaning dairy calves.

METHODS

At 22nd day of age, twenty-four Holstein calves were randomly assigned to one of two treatment groups (n = 12/treatment) based on milk feeding frequency (MF): i) 3 L of milk feeding two times a day; ii) 6 L of milk feeding once a day. The milk feeding amount was reduced to half for all calves between 56 and 60 days of age and weaning was done at 60 days of age. To determine the increase in weight and structural measurements, each calf was weighed and measured at 3 weeks of age and then at weaning. The daily behavioral activity of each calf was assessed from the 22nd day of age till weaning (60th day of age) through Nederlandsche Apparatenfabriek (NEDAP) software providing real-time data through a logger fitted on the calf's foot.

RESULTS

There was no interaction (p≥0.17) between MF and sex of the calves for routine behavioral parameters, body weight and structural measurements. Similarly, there was no effect of MF on routine behavioral parameters, body weight and structural measurements. However, the sex of the calves affected body weight gain in calves. Male calves had 27% greater total body weight and average daily gain than female calves. There was no effect of the sex of the calves on behavioral measurements. Collectively, in the current study, no negative effects of a once-a-day milk feeding regimen were found on routine behavioral and growth parameters of preweaning calves in group housing.

CONCLUSION

Once-a-day milk feeding can be safely adopted in preweaning calves from 22nd day of age.

Application of Precision Technologies to Characterize Animal Behavior: A Review

This study aims to evaluate the state of precision livestock farming (PLF)'s spread, utilization, effectiveness, and evolution over the years. PLF includes a plethora of tools, which can aid in a number of laborious and complex tasks. These tools are often used in the monitoring of different animals, with the objective to increase production and improve animal welfare. The most frequently monitored attributes tend to be behavior, welfare, and social interaction. This study focused on the application of three types of technology: wearable sensors, video observation, and smartphones. For the wearable devices, the focus was on accelerometers and global positioning systems. For the video observation, the study addressed drones and cameras. The animals monitored by these tools were the most common ruminants, which are cattle, sheep, and goats. This review involved 108 articles that were believed to be pertinent. Most of the studied papers were very accurate, for most tools, when utilized appropriate; some showed great benefits and potential.

Predictive models for disease detection in group-housed preweaning dairy calves using data collected from automated milk feeders

In the United States, dairy calves are typically housed individually due to the perception of reduced risk of spreading infectious diseases between calves and the ability to monitor health on an individual calf basis. However, automated milk feeders (AMF) can provide individual monitoring of group-housed calves while allowing them to express more natural feeding behaviors and interact with each other. Research has shown that feeding behaviors recorded by AMF can be a helpful screening tool for detecting disease in dairy calves. Altogether, there is an opportunity to use the data from AMF to create a more robust and efficient model to predict disease, reducing the need for visual observation. Therefore, the objective of this observational study was to predict disease in preweaning dairy calves using AMF feeding behavior data and machine learning (ML) algorithms. This study was conducted on a dairy farm located in the Upper Midwest United States and visited weekly from July 2018 to May 2019. During farm visits, AMF data and calves' treatment records were collected, and calves were visually health-scored for attitude, ear position, ocular discharge, nasal discharge, hide dirtiness, and cough score. The final datasets used for the analyses consisted of 740 and 741 calves, with 1,007 (healthy = 594 and sick = 413) and 1,044 (healthy = 560 and sick = 484) observations (health events) for Data 1 and Data 2, respectively. Data 1 included only the weekly calf health scores observed by research personnel, whereas Data 2 included primarily daily calf treatment records by farm staff in addition to weekly health scores. Calf visit-level feeding behaviors from AMF data included milk intake (mL/d), drinking speed (mL/min), visit duration (min), rewarded (with milk being offered) and unrewarded (without milk) visits (number per d), and the interval between visits (min). Three approaches were used to predict health status: generalized linear model, random forest, and gradient boosting machine. A total of 16 models were built using different combinations of behavior parameters, including the number of rewarded visits, number of unrewarded visits, visit duration, the interval between visits, intake, intake divided by rewarded visits, drinking speed, and drinking speed divided by rewarded visits, and also calf age at the sick day as predictor variables. Of all algorithms, random forest and gradient boosting had the best performance predicting the health status of dairy calves. The results indicated that weekly health scores were not enough to predict calf health status. However, daily treatment records and AMF data were sufficient for creating predictive algorithms (e.g., F1-scores of 0.775 and 0.784 for random forest and gradient boosting, Data 2). This study suggests that ML was effective in determining the specific visit-level feeding behaviors that can be used to predict disease in group-housed preweaning dairy calves. Implementing these ML algorithms could reduce the need for visual calf observation on farms, minimizing labor time and improving calf health.

Activity behaviors and relative changes in activity patterns recorded by precision technology were associated with diarrhea status in individually housed calves

The objective of this case-control study was to quantify any association of daily activity behaviors and relative changes in activity patterns (lying time, lying bouts, step count, activity index) with diarrhea status in preweaning dairy calves. Individually housed calves sourced from auction were health-scored daily for signs of diarrhea (fecal consistency loose or watery for 2 consecutive days) for the 28 d after arrival. Calves with diarrhea were pair-matched with healthy controls (n = 13, matched by arrival date, arrival weight, and diagnosis days to diarrheic calves). Mixed linear regression models were used to evaluate the association of diarrhea status, and the diarrhea status by day interaction with activity behaviors (d -3 to d 4) and relative changes in activity patterns (d -3 to d 4) relative to diagnosis of a diarrhea bout. The serum Brix percentage at arrival and daily temperature-humidity index from the calf barn were explored as quantitative covariates, with day as a repeated measure. The baseline for relative changes in activity patterns was set at 100% on d 0. Diarrheic calves were less active; they averaged fewer steps (119.1 ± 18.81 steps/d) than healthy calves (227.4 ± 18.81 steps/d, LSM ± SEM). Diarrheic calves also averaged lower activity indices (827.34 ± 93.092 daily index) than healthy calves (1,396.32 ± 93.092 daily index). We also found also a diarrhea status by day interaction for lying time on d -3, with diarrheic calves spending more time lying (20.80 ± 0.300 h/d) than healthy calves (19.25 ± 0.300 h/d). For relative changes in activity patterns, a diarrhea status by day interaction was detectable on d -2, where diarrheic calves had greater relative changes in step counts (diarrhea 634.85 ± 87.581% vs. healthy 216.51 ± 87.581%) and activity index (diarrhea 316.83 ± 35.692% vs. healthy 150.68 ± 35.692%). Lying bouts were not associated with diarrhea status. These results show that diarrheic calves were more lethargic, and they had relative changes in activity patterns 2 d before clinical signs of diarrhea. Future research should explore the potential of an activity alert that positively indicates an individually housed calf at risk for a diarrhea bout using deviations from relative changes in individual calf activity patterns.

Environmental and biological factors that influence feeding behavior of Holstein calves in automated milk feeding systems

Automated milk feeders (AMF) used for dairy calves continuously provide individual feeding behavior measurements. The objective of this retrospective cohort study was to evaluate the association between temperature-humidity index (THI), birth weight, and dam parity characteristics on feeding behavior (i.e., milk consumption and drinking speed). Historical data sets generated from a single commercial dairy farm, where healthy (not treated for bovine respiratory disease, enteric disease, or injury) Holstein calves were fed up to 24 L/d of milk, were used for the analysis. A total of 5,312 female Holstein calves born between August 2015 and August 2021 (mean birth weight ± standard deviation: 40.7 ± 4.7 kg) on a commercial dairy farm were fed up to 24 L/d of nonsaleable milk for the first 32 d. For the analyses, feeding behavior data from the AMF system were combined with demographic data from the farm management software, and weather records from the closest public weather station (7 km away). Linear mixed models used to analyze daily milk consumption and drinking speed included THI, birth weight, dam parity, and feeding day as fixed effects, and feeder and calf within feeder as random effects. These models explained 57% of the total variation in milk consumption and 48% of the variation in drinking speed. Calves born from primiparous cows had the lowest milk consumption and the greatest drinking speed in comparison to calves born from multiparous cows. Calves with heavier birth weights had higher milk consumption and faster drinking speed than lighter calves. Drinking speed was negatively associated with THI. Including data derived from individual calves and their environmental conditions in data sets exploring feeding behavior from AMF would control for variation and improve the predictive models for performance assessment.

Literature Review on Technological Applications to Monitor and Evaluate Calves' Health and Welfare

Precision livestock farming (PLF) research is rapidly increasing and has improved farmers' quality of life, animal welfare, and production efficiency. PLF research in dairy calves is still relatively recent but has grown in the last few years. Automatic milk feeding systems (AMFS) and 3D accelerometers have been the most extensively used technologies in dairy calves. However, other technologies have been emerging in dairy calves' research, such as infrared thermography (IRT), 3D cameras, ruminal bolus, and sound analysis systems, which have not been properly validated and reviewed in the scientific literature. Thus, with this review, we aimed to analyse the state-of-the-art of technological applications in calves, focusing on dairy calves. Most of the research is focused on technology to detect and predict calves' health problems and monitor pain indicators. Feeding and lying behaviours have sometimes been associated with health and welfare levels. However, a consensus opinion is still unclear since other factors, such as milk allowance, can affect these behaviours differently. Research that employed a multi-technology approach showed better results than research focusing on only a single technique. Integrating and automating different technologies with machine learning algorithms can offer more scientific knowledge and potentially help the farmers improve calves' health, performance, and welfare, if commercial applications are available, which, from the authors' knowledge, are not at the moment.

Feeding behaviors collected from automated milk feeders were associated with disease in group-housed dairy calves in the Upper Midwest United States

Automated milk feeders (AMF) are an attractive option for producers interested in adopting practices that offer greater behavioral freedom for calves and can potentially improve labor management. These feeders give farmers the opportunity to have a more flexible labor schedule and more efficiently feed group-housed calves. However, housing calves in group systems can pose challenges for monitoring calf health on an individual basis, potentially leading to increased morbidity and mortality. Feeding behavior recorded by AMF software could potentially be used as an indicator of disease. Therefore, the objective of this observational study was to investigate the association between feeding behaviors and disease in preweaning group-housed dairy calves fed with AMF. The study was conducted at a dairy farm located in the Upper Midwest United States and included a final data set of 599 Holstein heifer calves. The farm was visited on a weekly basis from May 2018, to May 2019, when calves were visually health scored and AMF data were collected. Calf health scores included calf attitude, ear position, ocular discharge, nasal discharge, hide dirtiness, cough score, and rectal temperatures. Generalized additive mixed models (GAMM) were used to identify associations between feeding behavior and disease. The final quasibinomial GAMM included the fixed (main and interactions) effects of feeding behavior at calf visit-level including milk intake (mL/d), drinking speed (mL/min), visit duration (min), rewarded (with milk being offered) and unrewarded (without milk) visits (number per day), and interval between visits (min), as well as the random effects of calf age in regard to their relationship with calf health status. Total milk intake (mL/d), drinking speed (mL/min), interval between visits (min) to the AMF, calf age (d), and rewarded visits were significantly associated with dairy calf health status. These results indicate that as total milk intake and drinking speed increased, the risk of calves being sick decreased. In contrast, as the interval between visits and age increased, the risk of calves being sick also increased. This study suggests that AMF data may be a useful screening tool for detecting disease in dairy calves. In addition, GAMM were shown to be a simple and flexible approach to modeling calf health status, as they can cope with non-normal data distribution of the response variable, capture nonlinear relationships between explanatory and response variables and accommodate random effects.

Associations between preweaning calf feeding behaviors with age at first calving and lactational performance using an automatic calf feeder

The objective of this retrospective observational study was to determine whether preweaning calf behaviors and milk replacer (MR) intake from an automatic calf feeder were associated with age at first calving and first-lactation performance. Calves were housed in groups with access to an automatic calf feeder for 7 wk with a maximum milk allowance of 1,800 g/d of MR (12 L/d). Outcomes of interest included age at first calving (n = 137), first-lactation mature-equivalent 305-d (ME305) milk yield (n = 132), and first-lactation ME305 energy-corrected milk (ECM) yield (n = 132). Linear models included the fixed effects of the daily means of unrewarded visits, rewarded visits, drinking speed, and MR intake. Furthermore, breed, disease diagnosis, season of birth, and age of the calf when it was first introduced to the automatic calf feeder were included in all models. The genetic parameter for milk yield (predicted transmitting ability for milk) was included in models related to lactational performance. Feeding behaviors and milk replacer intake were not associated with age at first calving. Unrewarded visits to the automatic calf feeder were associated with ME305 milk and ECM yields. As mean daily unrewarded visits increased by 1, first-lactation ME305 milk yield and ME305 ECM yield increased by 319 kg and 224 kg, respectively. No other feeding behavior was significantly associated with first-lactation ME305 milk or ECM yields. In conclusion, unrewarded visits were positively associated with first-lactation performance, but external validation is still needed.

Using Machine Learning and Behavioral Patterns Observed by Automated Feeders and Accelerometers for the Early Indication of Clinical Bovine Respiratory Disease Status in Preweaned Dairy Calves

The objective of this retrospective cohort study was to evaluate a K-nearest neighbor (KNN) algorithm to classify and indicate bovine respiratory disease (clinical BRD) status using behavioral patterns in preweaned dairy calves. Calves (N=106) were enrolled in this study, which occurred at one facility for the preweaning period. Precision dairy technologies were used to record feeding behavior with an automated feeder and activity behavior with a pedometer (automated features). Daily, calves were manually health-scored for bovine respiratory disease (clinical BRD; Wisconsin scoring system, WI, USA), and weights were taken twice weekly (manual features). All calves were also scored for ultrasonographic lung consolidation twice weekly. A clinical BRD bout (day 0) was defined as 2 scores classified as abnormal on the Wisconsin scoring system and an area of consolidated lung ≥3.0 cm2. There were 54 calves dignosed with a clinical BRD bout. Two scenarios were considered for KNN inference. In the first scenario (diagnosis scenario), the KNN algorithm classified calves as clinical BRD positive or as negative for respiratory infection. For the second scenario (preclinical BRD bout scenario), the 14 days before a clinical BRD bout was evaluated to determine if behavioral changes were indicative of calves destined for disease. Both scenarios investigated the use of automated features or manual features or both. For the diagnosis scenario, manual features had negligible improvements compared to automated features, with an accuracy of 0.95 ± 0.02 and 0.94 ± 0.02, respectively, for classifying calves as negative for respiratory infection. There was an equal accuracy of 0.98 ± 0.01 for classifying calves as sick using automated and manual features. For the preclinical BRD bout scenario, automated features were highly accurate at -6 days prior to diagnosis (0.90 ± 0.02), while manual features had low accuracy at -6 days (0.52 ± 0.03). Automated features were near perfectly accurate at -1 day before clinical BRD diagnosis compared to the high accuracy of manual features (0.86 ± 0.03). This research indicates that machine-learning algorithms accurately predict clinical BRD status at up to -6 days using a myriad of feeding behaviors and activity levels in calves. Precision dairy technologies hold the potential to indicate the BRD status in preweaned calves.

Social networks respond to a disease challenge in calves

Changes in network position and behavioral interactions have been linked with infectious disease in social animals. Here, we investigate the effects of an experimental disease challenge on social network centrality of group-housed Holstein bull dairy calves. Within group-housed pens (6/group) calves were randomly assigned to either a previously developed challenge model, involving inoculation with Mannheimia haemolytia (n = 12 calves; 3 calves/group) or a control involving only saline (n = 12 calves; 3 calves/group). Continuous behavioral data were recorded from video on pre-treatment baseline day and for 24 h following inoculation to describe social lying frequency and duration and all active social contact between calves. Mixed-model analysis revealed that changes in network position were related to the challenge. Compared to controls, challenged calves had reduced centrality and connectedness, baseline to challenge day. On challenge day, challenged calves were less central in the directed social contact networks (lower degree, strength and eigenvector centrality), and initiated contact (higher out-degree) with more penmates, compared to healthy calves. This finding suggests that giving rather than receiving affiliative social contact may be more beneficial for challenged calves. This is the first study demonstrating that changes in social network position coincide with an experimental challenge of a respiratory pathogen in calves.