Associations between feeding behaviors collected from an automated milk feeder and disease in group-housed dairy calves in Ontario: A cross-sectional study

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.

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.

Effect of Milk-Feeding Frequency and Calcium Gluconate Supplementation on Growth, Health, and Reproductive and Metabolic Features of Holstein Heifers at a Rearing Farm

We compared the effects of milk-feeding in 288 Holstein calves (72 per group) which were fed twice (2F) or thrice (3F) daily, with or without the addition of hydrogenated fat-embedded calcium gluconate (G) supplemented in the starter food and in the daily diet up to the age of 9 months, on the calves' metabolism, growth, health, and reproductive efficiency up to first pregnancy. The calves received 6 L of milk replacer (130 g/L) and had ad libitum access to water and textured calf starter with or without gluconate. Gluconate supplementation promoted a "catch-up" in growth in supplemented calves compared to their counterparts that did not receive gluconate. Gluconate appeared to reduce animal metabolic stress during key events, such as weaning and transfer into open-door pens, reducing fructosamine (352.61 vs. 303.06 in 3FG and 3F, respectively; p = 0.028) and urea (3F revealed the highest values compared with the other three groups: 19.06 for 3F vs. 13.9 (2F), 13.7 (2FG), and 14.3 (3FG), respectively, p = 0.002) from weaning onwards. The feeding of dairy calves with milk replacer three rather than two times per day tended to be associated with better health from weaning to 4 months old; parameters such as ultrasound lung score and calf health score improved over time (p < 0.001). Thrice-daily feeding with milk replacer tended to reduce the number of artificial inseminations per pregnancy in heifers by 0.2 points (p = 0.092). We confirmed significant correlations between early health and growth parameters and reproductive efficiency and a positive correlation between body weight and average daily weight gain and the thickness of the back fat layer in young heifers (r = 0.245; p < 0.0001; r = 0.214; p < 0.0001 respectively). Our study was conducted on a commercial farm with reasonably effective animal management, so baseline welfare was likely satisfactory.

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.

Behavioral activity patterns but not hair cortisol concentrations explain steers' transition-related stress in the first 6 wk in the feedlot

Stress during the transition of beef steers from ranch to feedlot may depend on steer source and preconditioning. The interplay between physiological and behavioral patterns of preconditioned (PC) and auction-derived (AD) steers, particularly after commingling, is poorly understood. Our objective was to evaluate whether hair cortisol (HC) concentrations were related to the health and performance of PC and AD steers and study behavioral activities after commingling over 6 wk in a feedlot. Steers, sourced either from ranch (PC, n = 250) or local auction (AD, n = 250), were assigned into 1 of 5 pens, 100% PC (100PC); 75% PC 25% AD (75PC); 50% PC 50% AD (50PC); 25% PC 75% AD (25PC), and 100% AD (0PC), each pen containing 100 steers. Pen was the experimental unit and individual steers were the observational unit where physiological and behavioral changes were measured. The study subsampled 225 steers (PC = 113 and AD = 112) which were equipped with CowManager ear tags to record behaviors. On day 40, hair samples from each steer were collected by clipping hair close to the skin. Data were analyzed using multiple linear, logistic regression, or multilevel negative binomial regression models depending on the outcomes. There was no difference in HC concentrations (day 40) between PC and AD steers (P = 0.66), and no association with Bovine Respiratory Disease (BRD)-related morbidity (P = 0.08) or average daily gain (ADG) (P = 0.44). After adjusting for source and commingling effects, HC concentrations did not affect time spent eating (P = 0.83), ruminating (P = 0.20), active (P = 0.89), or non-active (P = 0.32). PC steers spent more time eating and ruminating over weeks 1 to 4 (P < 0.01) and weeks 1 to 3, respectively (P < 0.05), and more time being active over weeks 1 and 2 compared to AD steers (P < 0.001), but less time being non-active than AD steers on weeks 1 to 3 (P < 0.001). Steers in 100PC and 50PC pens spent more time eating than steers in 0PC (P < 0.001), whereas steers in 25PC spent less time eating than steers in 0PC (P < 0.001). Steers in 0PC spent the most time being not active (P < 0.01). In conclusion, preconditioned steers spent more time eating, ruminating, and being active and less time being not active over the first 3 wk in the feedlot, regardless of commingling. The HC concentrations did not identify potentially lower stress related to ranch transfer and were neither associated with BRD-related morbidity nor ADG.

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.

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.

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.

Feeding behavior and activity levels are associated with recovery status in dairy calves treated with antimicrobials for Bovine Respiratory Disease

Calves with Bovine Respiratory Disease (BRD) have different feeding behavior and activity levels prior to BRD diagnosis when compared to healthy calves, but it is unknown if calves who relapse from their initial BRD diagnosis are behaviorally different from calves who recover. Using precision technologies, we aimed to identify associations of feeding behavior and activity with recovery status in dairy calves (recovered or relapsed) over the 10 days after first antimicrobial treatment for BRD. Dairy calves were health scored daily for a BRD bout (using a standard respiratory scoring system and lung ultrasonography) and received antimicrobial therapy (enrofloxacin) on day 0 of initial BRD diagnosis; 10–14 days later, recovery status was scored as either recovered or relapsed (n = 19 each). Feeding behaviors and activity were monitored using automated feeders and pedometers. Over the 10 days post-treatment, recovered calves showed improvements in starter intake and were generally more active, while relapsed calves showed sickness behaviors, including depressed feed intake, and longer lying times. These results suggest there is a new potential for precision technology devices on farms in evaluating recovery status of dairy calves that are recently treated for BRD; there is opportunity to automatically identify relapsing calves before re-emergence of clinical disease.

Associations between Feeding Behaviors Collected from an Automated Milk Feeder and Neonatal Calf Diarrhea in Group Housed Dairy Calves: A Case-Control Study

Simple Summary

Precision technology devices are often integrated on dairies to monitor animal health. One precision technology used to manage calves is an automated milk feeder which can record feeding behaviors such as daily milk intake, drinking speed, and feeder visits. The objective of this study was to determine if calf feeding behaviors collected by an automated milk feeder, changed in the days surrounding diagnosis of neonatal calf diarrhea (NCD; e.g., −3 days to 5 days after diagnosis). Milk intake was lower for the greatest number of days surrounding diagnosis of NCD compared to control calves, but the sensitivity and specificity of detecting NCD using any individual behavior was low. However, parallel testing using cumulative feeding behaviors on the day of diagnosis of NCD increased the sensitivity for detecting disease. This study provides insights into the association of feeding behavior with calves destined for NCD using an automated milk feeder. We suggest feeding behaviors cannot replace visual diagnosis of NCD, but that feeding behaviors might serve as a screening tool for producers.

Abstract

The objective of this case-control study was to determine if feeding behavior data collected from an automated milk feeder (AMF) could be used to predict neonatal calf diarrhea (NCD) in the days surrounding diagnosis in pre-weaned group housed dairy calves. Data were collected from two research farms in Ontario between 2017 and 2020 where calves fed using an AMF were health scored daily and feeding behavior data (milk intake (mL/d), drinking speed (mL/min), number of rewarded or unrewarded visits) was collected. Calves with NCD were pair matched to healthy controls (31 pairs) by farm, gender, and age at case diagnosis to assess for differences in feeding behavior between case and control calves. Calves were first diagnosed with NCD on day 0, and a NCD case was defined as calves with a fecal score of ≥2 for 2 consecutive days, where control calves remained healthy. Repeated measure mixed linear regression models were used to determine if there were differences between case and control calves in their daily AMF feeding behavior data in the days surrounding diagnosis of NCD (−3 to +5 days). Calves with NCD consumed less milk on day 0, day 1, day 3, day 4 and day 5 following diagnosis compared to control calves. Calves with NCD also had fewer rewarded visits to the AMF on day −1, and day 0 compared to control calves. However, while there was a NCD status x day interaction for unrewarded visits, there was only a tendency for differences between NCD and control calves on day 0. In this study, feeding behaviors were not clinically useful to make diagnosis of NCD due to insufficient diagnostic ability. However, feeding behaviors are a useful screening tool for producers to identify calves requiring further attention.