Evaluation of applying statistical process control techniques to daily average feeding behaviors to detect disease in automatically fed group-housed preweaned dairy calves

The development and validation of a milk feeding behavior alert from automated feeder data to classify calves at risk for a diarrhea bout: A diagnostic accuracy study

The objective of this diagnostic accuracy study was to develop and validate an alert to identify calves at risk for a diarrhea bout using milk feeding behavior data (behavior) from automated milk feeders (AMF). We enrolled Holstein calves (n = 259) as a convenience sample size from 2 facilities that were health scored daily preweaning and offered either 10 or 15 L/d of milk replacer. For alert development, 132 calves were enrolled and the ability of milk intake, drinking speed, and rewarded visits collected from AMF to identify calves at risk for diarrhea was tested. Alerts that had high diagnostic accuracy in the alert development phase were validated using a holdout validation strategy of 127 different calves from the same facilities (all offered 15 L/d) for -3 to 1 d relative to diarrhea diagnosis. We enrolled calves that were either healthy or had a first diarrheal bout (loose feces ≥2 d or watery feces ≥1 d). Relative change and rolling dividends for each milk feeding behavior were calculated for each calf from the previous 2 d. Logistic regression models and receiver operator curves (ROC) were used to assess the diagnostic ability for relative change and rolling dividends behavior relative to alert d) to classify calves at risk for a diarrhea bout from -2 to 0 d relative to diagnosis. To maximize sensitivity (Se), alert thresholds were based on ROC optimal classification cutoff. Diagnostic accuracy was met when the alert had a moderate area under the ROC curve (≥0.70), high accuracy (Acc; ≥0.80), high Se (≥0.80), and very high precision (Pre; ≥0.85). For alert development, deviations in rolling dividend milk intake with drinking speed had the best performance (10 L/d: ROC area under the curve [AUC] = 0.79, threshold ≤0.70; 15 L/d: ROC AUC = 0.82, threshold ≤0.60). Our diagnostic criteria were only met in calves offered 15 L/d (10 L/d: Se 75%, Acc 72%, Pre 92%, specificity [Sp] 55% vs. 15 L/d: Se 91%, Acc 91%, Pre 89%, Sp 73%). For holdout validation, rolling dividend milk intake with drinking speed met diagnostic criteria for one facility (threshold ≤0.60, Se 86%, Acc 82%, Pre 94%, Sp 50%). However, no milk feeding behavior alerts met diagnostic criteria for the second facility due to poor Se (relative change milk intake -0.36 threshold, Se 71%, Acc 70%, and Pre 97%). We suggest that changes in milk feeding behavior may indicate diarrhea bouts in dairy calves. Future research should validate this alert in commercial settings; furthermore, software updates, support, and new analytics might be required for on-farm application to implement these types of alerts.

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.

Intrinsic calf factors associated with the behavior of healthy pre-weaned group-housed dairy-bred calves

Technology-derived behaviors are researched for disease detection in artificially-reared calves. Whilst existing studies demonstrate differences in behaviors between healthy and diseased calves, intrinsic calf factors (e.g., sex and birthweight) that may affect these behaviors have received little systematic study. This study aimed to understand the impact of a range of calf factors on milk feeding and activity variables of dairy-bred calves. Calves were group-housed from ~7 days to 39 days of age. Seven liters of milk replacer was available daily from an automatic milk feeder, which recorded feeding behaviors and live-weight. Calves were health scored daily and a tri-axial accelerometer used to record activity variables. Healthy calves were selected by excluding data collected 3 days either side of a poor health score or a treatment event. Thirty-one calves with 10 days each were analyzed. Mixed models were used to identify which of live-weight, age, sex, season of birth, age of inclusion into the group, dam parity, birthweight, and sire breed type (beef or dairy), had a significant influence on milk feeding and activity variables. Heavier calves visited the milk machine more frequently for shorter visits, drank faster and were more likely to drink their daily milk allowance than lighter calves. Older calves had a shorter mean standing bout length and were less active than younger calves. Calves born in summer had a longer daily lying time, performed more lying and standing bouts/day and had shorter mean standing bouts than those born in autumn or winter. Male calves had a longer mean lying bout length, drank more slowly and were less likely to consume their daily milk allowance than their female counterparts. Calves that were born heavier had fewer lying and standing bouts each day, a longer mean standing bout length and drank less milk per visit. Beef-sired calves had a longer mean lying bout length and drank more slowly than their dairy sired counterparts. Intrinsic calf factors influence different healthy calf behaviors in different ways. These factors must be considered in the design of research studies and the field application of behavior-based disease detection tools in artificially reared calves.

Performance of commercial growing-finishing pigs fed supplemental isoquinoline alkaloids: a statistical process control analysis

BACKGROUND

Statistical Process Control (SPC) is a powerful statistical tool that can be used in animal production to evaluate the evolution of production parameters overtime in response to the implementation of a specific strategy. The aim of this study was to evaluate the effect of supplementing growing-finishing pigs with isoquinoline alkaloids (IQ) on growth performance parameters by using the SPC method. IQ are natural secondary plant metabolites which have been extensively investigated in food animals due to their efficacy in supporting growth performance and the overall health status. Performance parameters and medication usage were collected from 1,283,880 growing-finishing pigs fed the same basal diet, 147,727 of which were supplemented with IQ from day 70 of life until slaughter.

RESULTS

Supplementation with IQ improved feed conversion ratio, while feed intake and daily gain were maintained.

CONCLUSION

SPC methods are useful statistical tools to evaluate the effect of using a new feed additive in the feed of pigs on growth performance at a commercial level. Additionally, IQ supplementation improved growth performance and it can be considered as a good strategy to reduce feed conversion in growing-finishing pigs.

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.

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.

Sucking and drinking behaviour in preweaned dairy calves in the first five weeks of life

Background

Nursing and sucking are essential for adequate nourishment of preweaned calves and the relationship between sucking indices has not been studied. The goal of this study was to investigate the number of sucks per litre of milk and per minute of drinking and the amount of milk ingested per suck in healthy preweaned calves. Correlation coefficients were calculated for the relationships between these variables. Eighteen healthy calves were used from birth to 5 weeks of age, and five measurements were made at the end of weeks 1 to 5. The calves were randomly divided into three groups and offered milk twice daily in a bucket with a rubber nipple. The amount of milk offered per day was equal to 12% of body weight in group A and 16% of body weight in group B. Calves in group C were offered as much milk as they wanted during each feeding period. The duration of drinking was determined with a stopwatch, and the number of sucks was counted with a handheld tally counter. The variables drinking duration, total amount consumed and the number of sucks required were used to calculate the number of sucks/min, the number of sucks/L, the amount ingested per suck and drinking speed.

Results

The number of sucks/min ranged from 113 to 133 and increased significantly during the study period. The mean number of sucks/L decreased from 204 in week 1 to 141 in week 5 and drinking speed increased from 0.6 to 1.0 L/min. There were significant correlations between the number of sucks/L of milk and the amount of milk ingested per suck, drinking duration, total amount consumed and drinking speed. Drinking speed was positively correlated with the amount of milk ingested per suck and the total amount of milk consumed, and negatively correlated with drinking duration.

Conclusions

These findings show that drinking variables of calves offered different amounts of milk vary little and significant changes occur during the same period with respect to the number of sucks/L of milk and the amount of milk ingested per suck. Several drinking variables are significantly correlated with other variables.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-022-03280-x.

Early Detection of Respiratory Diseases in Calves by Use of an Ear-Attached Accelerometer

Simple Summary

Bovine respiratory disease is one of the most important diseases in group-housed calves worldwide, with impacts on calf welfare and farm economics. Early detection of the disease is important for the well-being of the animals and a targeted treatment. Therefore, tools for an automated monitoring of individual calves would be a breakthrough in health management. In this study, we used an ear-attached accelerometer to evaluate its potential for the early detection of behavioral changes related to respiratory disease in calves. Our result showed that accelerometers are able to detect changes in activity and lying times that can be used to predict respiratory disease before clinical diagnosis.

Abstract

Accelerometers (ACL) can identify behavioral and activity changes in calves. In the present study, we examined the association between bovine respiratory disease (BRD) and behavioral changes detected by an ear-tag based ACL system in weaned dairy calves. Accelerometer data were analyzed from 7 d before to 1 d after clinical diagnosis of BRD. All calves in the study (n = 508) were checked daily by an adapted University of Wisconsin Calf Scoring System. Calves with a score ≥ 4 and fever for at least two consecutive days were categorized as diseased (DIS). The day of clinical diagnosis of BRD was defined as d 0. The data analysis showed a significant difference in high active times between DIS and healthy control calves (CON), with CON showing more high active times on every day, except d −3. Diseased calves showed significantly more inactive times on d −4, −2, and 0, as well as longer lying times on d −5, −2, and +1. These results indicate the potential of the ACL to detect BRD prior to a clinical diagnosis in group-housed calves. Furthermore, in this study, we described the ‘normal’ behavior in 428 clinically healthy weaned dairy calves obtained by the ACL system.

Repeatability and Predictability of Calf Feeding Behaviors-Quantifying Between- and Within-Individual Variation for Precision Livestock Farming

Individual calves show substantial between- and within-individual variation in their feeding behavior, the existence and extent of which are not fully researched. In this study, 57,196 feeding records, collected by a computerized milk feeder from 48 pre-weaned calves over 5 weeks, were collated and analyzed for individual differences in three different feeding behaviors using a multi-level modeling approach. For each feeding behavior, we quantified behavioral variation by calculating repeatability and the coefficient of variation in predictability. Our results indicate that calves differed from each other in their average behavioral expression (behavioral type) and in their residual, within individual variation around their behavioral type (predictability). Feeding rate and total meals had the highest repeatability (>0.4) indicating that substantial, temporally stable between-individual differences exist for these behaviors. Additionally, for some behaviors (e.g., feeding rate) calves varied from more to less predictable whereas for other behaviors (e.g., meal size) calves were more homogenous in their within-individual variation around their behavioral type. Finally, we show that for individual calves, behavioral types for feeding rate and total meals were positively correlated which may suggest the existence of an underlying factor responsible for driving the (co)expression of these two behaviors. Our results highlight how the application of methods from the behavioral ecology literature can assist in improving our understanding of individual differences in calf feeding behavior. Furthermore, by uncovering consistencies between individual behavioral differences in calves, our results indicate that animal personality may play a role in driving variability in calf feeding behavior.

A Systematic Review of Automatic Health Monitoring in Calves: Glimpsing the Future From Current Practice

Infectious diseases, particularly bovine respiratory disease (BRD) and neonatal calf diarrhea (NCD), are prevalent in calves. Efficient health-monitoring tools to identify such diseases on time are lacking. Common practice (i.e., health checks) often identifies sick calves at a late stage of disease or not at all. Sensor technology enables the automatic and continuous monitoring of calf physiology or behavior, potentially offering timely and precise detection of sick calves. A systematic overview of automated disease detection in calves is still lacking. The objectives of this literature review were hence: to investigate previously applied sensor validation methods used in the context of calf health, to identify sensors used on calves, the parameters these sensors monitor, and the statistical tools applied to identify diseases, to explore potential research gaps and to point to future research opportunities. To achieve these objectives, systematic literature searches were conducted. We defined four stages in the development of health-monitoring systems: (1) sensor technique, (2) data interpretation, (3) information integration, and (4) decision support. Fifty-four articles were included (stage one: 26; stage two: 19; stage three: 9; and stage four: 0). Common parameters that assess the performance of these systems are sensitivity, specificity, accuracy, precision, and negative predictive value. Gold standards that typically assess these parameters include manual measurement and manual health-assessment protocols. At stage one, automatic feeding stations, accelerometers, infrared thermography cameras, microphones, and 3-D cameras are accurate in screening behavior and physiology in calves. At stage two, changes in feeding behaviors, lying, activity, or body temperature corresponded to changes in health status, and point to health issues earlier than manual health checks. At stage three, accelerometers, thermometers, and automatic feeding stations have been integrated into one system that was shown to be able to successfully detect diseases in calves, including BRD and NCD. We discuss these findings, look into potentials at stage four, and touch upon the topic of resilience, whereby health-monitoring system might be used to detect low resilience (i.e., prone to disease but clinically healthy calves), promoting further improvements in calf health and welfare.

Clinical Scores in Veterinary Medicine: What Are the Pitfalls of Score Construction, Reliability, and Validation? A General Methodological Approach Applied in Cattle

Simple Summary

Clinical scores are practical tools that can be used in the daily management of cattle. Score building and validation are a challenge involving various methodological and statistical issues. This article provides a specific framework for clinical score building where the target condition can be assessed directly or indirectly. Practical examples are given throughout the manuscript in order to build new scores or to assess score robustness.

Abstract

Clinical scores are commonly used for cattle. They generally contain a mix of categorical and numerical variables that need to be assessed by scorers, such as farmers, animal caretakers, scientists, and veterinarians. This article examines the key concepts that need to be accounted for when developing the test for optimal outcomes. First, the target condition or construct that the scale is supposed to measure should be defined, and if possible, an adequate proxy used for classification should be determined. Then, items (e.g., clinical signs) of interest that are either caused by the target condition (reflective items) or that caused the target condition (formative items) are listed, and reliable items (inter and intra-rater reliability) are kept for the next step. A model is then developed to determine the relative weight of the items associated with the target condition. A scale is then built after validating the model and determining the optimal threshold in terms of sensitivity (ability to detect the target condition) and specificity (ability to detect the absence of the target condition). Its robustness to various scenarios of the target condition prevalence and the impact of the relative cost of false negatives to false positives can also be assessed to tailor the scale used based on specific application conditions.

Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows

Simple Summary

The management of body condition scores of dairy cows during the dry period affects postpartum health. It is therefore important to monitor the body condition scores so as to predict the occurrence of postpartum diseases. In this study, statistical process control technology was used to monitor body condition scores and to explore its effects on nutritional strategies and the development of postpartum subclinical ketosis. The results showed that the statistical process control technology was not a good tool for the diagnosis of postpartum subclinical ketosis, but an appropriate tool for the guidance of nutritional strategies.

Abstract

The management of body condition score (BCS) during the dry period is associated with the postpartum health outcomes of dairy cows. However, the difference between the actual BCS and the fixed ideal value is not able to accurately predict the occurrence of postpartum diseases. This study aimed to use statistical process control (SPC) technology to monitor the BCS of dry cows, to evaluate the effect of control charts on nutritional strategies, and to explore the utility of SPC in predicting the incidence of postpartum subclinical ketosis (SCK). The BCS and SCK data of 286 cows from the dry off period to 60 days postpartum were collected to set up the early warning function. Three control charts, including a control chart for the average BCS of the herds, for the BCS of each dry cow, and for individual BCS, were established. The early warning signs for postpartum SCK development were: (1) an individual BCS more than 3.5 that remained unchanged for six weeks; (2) a capability index (CPK), an SPC tool, greater than −0.52. Using these parameters, the early warning signs of SCK development were verified in 429 dry cows. The results showed that the accuracy of early warning signal was 0.64 and the precision was 0.26. The control chart showed that the average BCS of dry cows was consistently higher than the expected upper limit of BCS during the experimental period, and that the addition of new cows to the herds increased the average BCS. In summary, the application of SPC technology to monitor the BCS of dry cows was not a good tool for the prediction of postpartum SCK occurrence but was an appropriate tool for guiding positive nutrition strategies.

A survey of calf management practices and farmer perceptions of calf housing in UK dairy herds

Adoption of optimal management techniques for rearing dairy calves has significant effects on their health, welfare, and productivity. Despite much published literature on best practice, calf morbidity and mortality rates remain high. This survey aimed to establish current calf management practices in the United Kingdom, along with farmer perceptions surrounding different housing types. A survey containing 48 questions was distributed online to UK farmers via social media, online forums, and a convenience sample of veterinary practices and was completed by 216 participants. A descriptive analysis with frequency distributions was calculated, with chi-squared tests, linear regression and multinomial regression performed to assess associations between variables. There was a low level of regular veterinary involvement in day-to-day health decision making for calves (3/216, 1.4%), highlighting the need for appropriate staff training and standard operating procedures to ensure prudent antimicrobial usage. Restricted calf milk feeding remains highly prevalent in the United Kingdom, with most calves fed milk replacer (114/216, 52.8%), twice daily (189/216, 87.5%), initially given milk at 4 L/d (66/216, 30.6%) or 6 L/d (47/216, 21.8%). There was, however, a small number of farmers initially feeding only 2 to 3 L/d (28/216, 13.0%). Euthanasia of bull calves (5/216, 2.3%) and feeding antimicrobial waste milk to calves (8/216, 3.7%) both occurred on some farms. With regard to housing, use of individual calf pens has reduced from around 60% in 2010 to 38.4% in this study (83/216), with this reduction being partly driven by the policy of UK milk buyers. Farmer perceptions indicated that individual housing was thought to help to improve calf health and feed monitoring of calves, suggesting that successful use of group housing requires a higher level of stockmanship. The majority of farmers did not provide fresh bedding to calves on a daily basis (141/216, 65.3%), and relatively few disinfected both the calf housing (38.0%) and ground (47.7%) between calves, suggesting that hygiene practices may require additional attention in farm management protocols.

Do we automatically detect health- or general welfare-related issues? A framework

The early detection of health disorders is a central goal in livestock production. Thus, a great demand for technologies enabling the automated detection of such issues exists. However, despite decades of research, precision livestock farming (PLF) technologies with sufficient accuracy and ready for implementation on commercial farms are rare. A central factor impeding technological development is likely the use of non-specific indicators for various issues. On commercial farms, where animals are exposed to changing environmental conditions, where they undergo different internal states and, most importantly, where they can be challenged by more than one issue at a time, such an approach leads inevitably to errors. To improve the accuracy of PLF technologies, the presented framework proposes a categorization of the aim of detection of issues related to general welfare, disease and distress and defined disease. Each decision level provides a different degree of information and therefore requires indicators varying in specificity. Based on these considerations, it becomes apparent that while most technologies aim to detect a defined health issue, they facilitate only the identification of issues related to general welfare. To achieve detection of specific issues, new indicators such as rhythmicity patterns of behaviour or physiological processes should be examined.

Predicting morbidity and mortality using automated milk feeders: A scoping review

Automated milk feeders (AMF) are computerized systems that provide producers with a tool that can be used to more efficiently raise dairy calves and allow for easier implementation of a high plane of nutrition during the milk feeding phase. Automated milk feeders also have the ability to track individualized behavioral data, such as milk consumption, drinking speed, and the number of rewarded and unrewarded visits to the feeder, that could potentially be used to predict disease development. The objective of this scoping review was to characterize the body of literature investigating the use of AMF data to predict morbidity and mortality in dairy calves during the preweaning stage. This review lists the parameters that have been examined for associations with disease in calves and identify discrepancies found in the literature. Five databases and relevant conference proceedings were searched. Eligible studies focused on the use of behavioral parameters measured by AMF to predict morbidity or mortality in preweaned dairy calves. Two reviewers independently screened titles and abstracts from 6,675 records identified during the literature search. After title and abstract screening, 382 studies were included and then assessed at the full-text level. Of these, 56 studies fed calves using an AMF and provided some measure of morbidity or mortality. Thirteen examined AMF parameters for associations with morbidity or mortality. The studies were completed in North America (n = 6), Europe (n = 6), and New Zealand (n = 1). The studies varied in sample size, ranging from 30 to 1,052 calves with a median of 100 calves. All 13 studies included enteric disease as an outcome and 11 studies evaluated respiratory disease. Of the studies measuring enteric disease, 8 provided disease definitions (n = 8/13, 61.2%); however, for respiratory disease, only 5 provided a disease definition (n = 5/11, 45.5%). Disease definitions and thresholds varied greatly between studies, with 10 using some form of health scoring. When evaluating feeding metrics as indicators of disease, all 13 studies investigated milk consumption and 6 and 7 studies investigated drinking speed and number of rewarded and unrewarded visits, respectively. Overall, this scoping review identified that daily milk consumption, drinking speed, and rewarded and unrewarded visits may provide insight into early disease detection in preweaned dairy calves. However, the disparity in reporting of study designs and results between included studies made comparisons challenging. In addition, to aid with the interpretation of studies, standardized disease outcomes should be used to improve the utility of this primary research.

Randomized controlled trial assessing the effects of xylazine sedation in 2- to 6-week-old dairy calves disbudded with a cautery iron

The use of local anesthesia and a nonsteroidal anti-inflammatory drug (NSAID) can reduce indicators of pain and inflammation and encourage self-rewarding behavior in calves following disbudding. Although the use of sedation may be recommended as a best practice for disbudding, there is little research in this area. The objective of this study was to evaluate the effects of xylazine sedation in conjunction with a local anesthetic and an NSAID in calves undergoing cautery disbudding. One hundred twenty-two group-housed female and male Holstein calves fed milk with automated feeders, aged 13 to 44 d, were enrolled over 9 replicates and randomly allocated to 1 of 2 treatments: (1) sedated: lidocaine cornual nerve block, 0.5 mg/kg meloxicam (administered subcutaneously) and 0.2 mg/kg xylazine (administered intramuscularly), or (2) nonsedated: lidocaine cornual nerve block and meloxicam. Outcomes collected consisted of feeding behavior (collected using automated milk feeders), latency to drink milk following disbudding, play behavior (induced by adding bedding), lying behavior, mechanical nociceptive threshold (MNT, measured using a pressure force algometer), struggling behavior during disbudding, length of time to administer the nerve block, length of time to disbud, and serum haptoglobin concentrations. Data were analyzed using mixed models with a fixed effect for baseline values and a random effect for trial replicate. Linear regression was used to assess continuous outcomes, logistic regression for binary outcomes, and Poisson and negative binomial models for count data with negative binomial models used if the over dispersion term was significant. There were no detected differences between the treatment groups in mean daily milk consumption in the 72-h following disbudding. Sedated calves had reduced average milk drinking speed from 0 to 24 h and 24 to 48 h following disbudding compared with nonsedated calves, but no difference was detected from 48 to 72 h. Sedated calves had reduced MNT at 0, 60, and 240 min after disbudding, but no differences were detected between groups at 24 h after disbudding. Nonsedated calves had 4.5 times the odds (95% CI: 1.5-13.2) of struggling more than twice during the disbudding procedure compared with sedated calves, and it took less time to administer a nerve block to sedated calves compared with nonsedated. At +3 h, nonsedated calves were 79 times (95% CI: 22.4 to 279.2) more likely to play compared with sedated calves, and 24 h after disbudding, sedated calves were 2 times more likely to play compared with nonsedated calves (95% CI: 0.93-4.3). The results indicate that calves sedated with xylazine for cautery disbudding responded less to painful stimuli (disbudding and MNT) both during and following the procedure and had a higher rate of play behavior 24 h following sedation compared with the nonsedated calves, but xylazine may also have a prolonged carryover effect that affects suckling behavior for 48 h following sedation.

Symposium review: Precision technologies for dairy calves and management applications

There is an increasing interest in using precision dairy technologies (PDT) to monitor real-time animal behavior and physiology in livestock systems around the world. Although PDT in adult cattle is extensively reviewed, PDT use for the management of preweaned dairy calves has not been reviewed. We systematically reviewed research on the use and application of precision technologies in calves. Accelerometers have the potential to be used to monitor lying behavior, step activity, and rumination, which are useful to detect changes in behavior that may be indicative of disease, responses to painful procedures, or positive welfare behaviors such as play. Automated calf feeding systems can control delivery of nutritional plans to individualize feeding and weaning of calves; changes in feeding behaviors (such as milk intake, drinking speed, and unrewarded visits) may also be used to identify early onset of disease. The PDT devices also measure physiological and physical attributes in dairy calves. For instance, temperature monitoring devices such as infrared thermography, ruminal boluses, and implanted microchips have been assessed in calves, but no herd management-based commercial system is available. Many other PDT are in development with potential to be used in dairy calf management, such as image and acoustic-based monitoring, real-time location, and use of enrichment items for monitoring positive emotional states. We conclude that PDT have great potential for application in dairy calf management, enabling precise behavioral and physiological monitoring, targeted feeding programs, and identification of calves with poor health or behavioral impairments. We strongly encourage further development and validation of commercially available technologies for on-farm application of the monitoring of dairy calf welfare, performance, and health.

Short Communication: Association between neonatal calf diarrhea and lying behaviors

The objective of this study was to determine the association of neonatal calf diarrhea (NCD) with step activity and lying behaviors in pre-weaned dairy calves. Calves were housed in individual hutches for the first 6 days of life, and then moved into a group pen. On the day of birth, calves (n = 30) were fitted with an accelerometer, and step activity and lying behaviors were recorded. Calves were assigned a fecal score (FS) twice daily using a 0 to 3 scale, and were diagnosed with NCD (n = 10) when the score was a 3. To ensure the only association noted was due to NCD, calves that had any other health complications were excluded from analyses (n = 1). Calves with NCD were pair matched by age, breed, and birthdate to a healthy calf. Day 0 was designated as the date of NCD diagnosis. Calves with NCD spent less time lying (P < 0.05) and displayed more lying bouts (P < 0.05) of a shorter duration (P < 0.01) than healthy calves. Specifically, calves with NCD displayed more lying bouts on days -7 (P < 0.05), -6 (P < 0.01), -5 (P < 0.01), -4 (P < 0.01), and -3 (P < 0.05). Similarly, lying bout duration was shorter for calves with NCD on days -6 (P < 0.05), -5 (P < 0.05), -4 (P < 0.01), and -3 (P < 0.01). Additional research is needed to examine if these tools can be used to identify diseased calves prospectively.