Changes in behaviour of dairy cows with clinical mastitis

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.

Multivariable time series classification for clinical mastitis detection and prediction in automated milking systems

In this study, we developed a machine learning framework to detect clinical mastitis (CM) at the current milking (i.e., the same milking) and predict CM at the next milking (i.e., one milking before CM occurrence) at the quarter level. Time series quarter-level milking data were extracted from an automated milking system (AMS). For both CM detection and prediction, the best classification performance was obtained from the decision tree-based ensemble models. Moreover, applying models on a data set containing data from the current milking and past 9 milkings before the current milking showed the best accuracy for detecting CM; modeling with a data set containing data from the current milking and past 7 milkings before the current milking yielded the best results for predicting CM. The models combined with oversampling methods resulted in specificity of 95 and 93% for CM detection and prediction, respectively, with the same sensitivity (82%) for both scenarios; when lowering specificity to 80 to 83%, undersampling techniques facilitated models to increase sensitivity to 95%. We propose a feasible machine learning framework to identify CM in a timely manner using imbalanced data from an AMS, which could provide useful information for farmers to manage the negative effects of CM.

Cow characteristics associated with the variation in number of contacts between dairy cows

In modern freestall barns where large groups of cows are housed together, the behavior displayed by herd mates can influence the welfare and production of other individuals. Therefore, understanding social interactions in groups of dairy cows is important to enhance herd management and optimize the outcomes of both animal health and welfare in the future. Many factors can affect the number of social contacts in a group. This study aimed to identify which characteristics of a cow are associated with the number of contacts it has with other group members in 2 different functional areas (feeding and resting area) to increase our understanding of the social behavior of dairy cows. Inside 2 herds housed in freestall barns with around 200 lactating cows each, cow positions were recorded with an ultra-wideband real-time location system collecting all cows' positions every second over 2 wk. Using the positioning data of the cows, we quantified the number of contacts between them, assuming that cows spending time in proximity to one another (within a distance of 2.5 m for at least 10 min per day) were interacting socially. We documented in which barn areas these interactions occurred and used linear mixed models to investigate if lactation stage, parity, breed, pregnancy status, estrus, udder health, and claw health affect the number of contacts. We found variation in the number of contacts a cow had between individuals in both functional areas. Cows in later lactation had more contacts in the feeding area than cows in early lactation. Furthermore, in one herd, higher parity cows had fewer contacts in the feeding area than first parity cows, and in the other herd, cows in third parity or higher had more contacts in the resting area. This study indicates that cow characteristics such as parity and days in milk are associated with the number of contacts a cow has daily to its herd mates and provides useful information for further research on social interactions of dairy cows.

The impact of health disorders on automated sensor measures and feed intake in lactating Holstein dairy cattle

Animal health and feed intake are closely interrelated, with the latter being an important indicator of an animal’s health status. Automated sensors for dairy cattle have been developed to detect changes in indicators of health, such as decreased rumination or activity. Previous studies have identified associations between sensor measurements and feed intake. Thus, the objective of this study was to determine if health disorders impact the associations identified between sensors and dry matter intake (DMI), and to measure the impact of health disorders on DMI. A total of 934 cows with health disorders (lameness, mastitis, and other), of which 57, 94, and 333 cows had observations for a rumen bolus and one of two ear tags, were analyzed to determine how health disorders impact the association of sensors with DMI. Eleven sensor measurements were collected across the three sensors, including total and point-in-time activity, rumination time, inner-ear temperature, rumen pH and rumen temperature. Associations of health disorders and sensor measures with DMI were evaluated when accounting for systematic effects (i.e., contemporary group, parity, and days in milk) and energy sinks accounted for in determination of feed efficiency (e.g., milk production, body weight and composition). In order to determine if inclusion of health disorders or sensor measures improved model fit, model AICs were assessed. Health disorders were significantly associated with all sensor measurements (P< 0.0001), with the direction of association dependent on sensor measure and health disorder. Moreover, DMI decreased with all health disorders, with larger impacts observed in animals in third and higher lactations. Numerous sensor measurements were associated with DMI, including when DMI was adjusted for energy sink variables and health. Inclusion of rumen bolus temperature, rumination or activity with health data reduced model AIC when evaluating DMI as the dependent variable. Some sensor measures, including measurements of activity, temperature and rumination, accounted for additional variation in feed intake when adjusted for health disorders. Results from the study indicate that feed intake and sensor measures are impacted by health disorders. These findings may have implications for use of sensors in genetic evaluations and precision feeding of dairy cattle.

Early detection of mastitis in cows using the system based on 3D motions detectors

Mastitis is one of the major health problems in dairy herds leading to a reduction in the leading to a reduction in the quality of milk and economic losses. The research aimed to present the system, which uses electronic 3D motion detectors to detect the early symptoms of mastitis. The system would allow more effective prevention of this illness. The experiment was carried out on 118 cows (64 Holstein Friesian and 54 Brown Swiss). The animals were kept in free-stall barn with access to pasture. The occurrence of mastitis cases was noticed in veterinary register. Microbiological culture was taken from milk in order to confirm the development of infection. Data from motion detectors were defined as time spent by animals on feed intake, ruminating, physical activity and rest, and were expanded by adding information about feeding group, breed type and lactation number. During analyses, two approaches were used to process the same dataset: artificial neural networks (ANN) and logistic regression. The obtained ANN and the logistic regression models proved to be satisfactory from the perspective of applied criteria of goodness of fit (area under curve-exceed 0.8). Quality parameters (accuracy, sensitivity and specifity) of logistic regression are relatively high (larger than 0.73), whereas the ranks of significance of the studied variables varied across datasets. These proposed models can be useful for automating the detection of mastitis once integrated into the farm's IT system.

Behavioral Fingerprinting: Acceleration Sensors for Identifying Changes in Livestock Health

During disease or toxin challenges, the behavioral activities of grazing animals alter in response to adverse situations, potentially providing an indicator of their welfare status. Behavioral changes such as feeding behavior, rumination and physical behavior as well as expressive behavior, can serve as indicators of animal health and welfare. Sometimes behavioral changes are subtle and occur gradually, often missed by infrequent visual monitoring until the condition becomes acute. There is growing popularity in the use of sensors for monitoring animal health. Acceleration sensors have been designed to attach to ears, jaws, noses, collars and legs to detect the behavioral changes of cattle and sheep. So far, some automated acceleration sensors with high accuracies have been found to have the capacity to remotely monitor the behavioral patterns of cattle and sheep. These acceleration sensors have the potential to identify behavioral patterns of farm animals for monitoring changes in behavior which can indicate a deterioration in health. Here, we review the current automated accelerometer systems and the evidence they can detect behavioral patterns of animals for the application of potential directions and future solutions for automatically monitoring and the early detection of health concerns in grazing animals.

Associations between Gastrointestinal Nematode Infection Burden and Lying Behaviour as Measured by Accelerometers in Periparturient Ewes

Simple Summary

Novel sensor technologies have great potential to improve animal health and welfare on farms by identifying disease early in livestock. These technologies are yet to be widely applied in sheep flocks despite their great potential to aid control of costly disease such as those caused by parasitic infection. In this study, leg-attached accelerometer sensors recorded the behaviour of 54 ewes in late pregnancy, with the aim of discovering if gastrointestinal nematode (GIN) infection levels were associated with behavioural variation. It was found that ewes laid down more often on average when infected with increasing numbers of GIN. Each lying bout was also shorter in length on average in ewes infected with higher levels of GIN. The results demonstrate that ewe behaviour can be an indication of parasite infection levels, and thus automated monitoring of sheep behaviour could allow animals to be treated efficiently against GIN in the future, maximising animal health and minimising production losses.

Abstract

The application of precision livestock farming (PLF) technologies will underpin new strategies to support the control of livestock disease. However, PLF technology is underexploited within the sheep industry compared to other livestock sectors, and research is essential to identify opportunities for PLF applications. These opportunities include the control of endemic sheep disease such as parasitic gastroenteritis, caused by gastrointestinal nematode infections, which is estimated to cost the European sheep industry EUR 120 million annually. In this study, tri-axial accelerometers recorded the behaviour of 54 periparturient Welsh Mule ewes to discover if gastrointestinal nematode (GIN) infection burden, as measured by faecal egg count (FEC), was associated with behavioural variation. Linear mixed models identified that increasing FECs in periparturient ewes were significantly associated with a greater number of lying bouts per day and lower bout durations (p = 0.013 and p = 0.010, respectively). The results demonstrate that FECs of housed periparturient ewes are associated with detectable variations in ewe behaviour, and as such, with further investigation there is potential to develop future targeted selective treatment protocols against GIN in sheep based on behaviour as measured by PLF technologies.

Escherichia coli Mastitis in Dairy Cattle: Etiology, Diagnosis, and Treatment Challenges

Bovine mastitis is an inflammation of the udder tissue parenchyma that causes pathological changes in the glandular tissue and abnormalities in milk leading to significant economic losses to the dairy industry across the world. Mammary pathogenic Escherichia (E.) coli (MPEC) is one of the main etiologic agents of acute clinical mastitis in dairy cattle. MPEC strains have virulence attributes to resist the host innate defenses and thrive in the mammary gland environment. The association between specific virulence factors of MPEC with the severity of mastitis in cattle is not fully understood. Furthermore, the indiscriminate use of antibiotics to treat mastitis has resulted in antimicrobial resistance to all major antibiotic classes in MPEC. A thorough understanding of MPEC’s pathogenesis and antimicrobial susceptibility pattern is required to develop better interventions to reduce mastitis incidence and prevalence in cattle and the environment. This review compiles important information on mastitis caused by MPEC (e.g., types of mastitis, host immune response, diagnosis, treatment, and control of the disease) as well as the current knowledge on MPEC virulence factors, antimicrobial resistance, and the dilemma of MPEC as a new pathotype. The information provided in this review is critical to identifying gaps in knowledge that will guide future studies to better design diagnostic, prevent, and develop therapeutic interventions for this significant dairy disease.

Changes in social and feeding behaviors, activity, and salivary serum amyloid A in cows with subclinical mastitis

The aim of this study was to identify detailed changes in behavior, and in salivary serum amyloid A (SAA), associated with subclinical mastitis. This included standard sickness behaviors, such as decreased activity, feeding and drinking (here labeled "core maintenance" behaviors), and less well-studied social, grooming, and exploratory behaviors (here labeled "luxury" behaviors). Luxury behaviors are biologically predicted to change at lower levels of mastitis infection and are, therefore, particularly relevant to detecting subclinical mastitis. Salivary serum amyloid A is a physiological marker of systemic inflammation, with levels in milk and serum already known to increase during subclinical mastitis. We investigated whether the same was true for SAA in cow saliva. Data were collected for 17 matched pairs of commercial barn-housed Holstein-Friesian cows. Each pair comprised a cow with subclinical mastitis (SCM) and a healthy control (CTRL), identified using somatic cell count (SCC; SCM: SCC >200 × 1,000 cells/mL; CTRL: SCC <100 × 1,000 cells/mL). SCM cows were selected for study ad hoc, at which point they were paired with a CTRL cow, based upon parity and calving date; consequently, the full data set was accrued over several months. Data were collected for each pair over 3 d: SCC (d 1), behavior (d 2), salivary SAA (d 3). All behaviors performed by the focal cows over a single 24-h period were coded retrospectively from video footage, and differences between the SCM and CTRL groups were investigated using the main data set and a subset of data corresponding to the hour immediately following morning food delivery. Saliva was collected using cotton swabs and analyzed for SAA using commercially available ELISA kits. We report, for the first time, that an increase in salivary SAA occurs during subclinical mastitis; SAA was higher in SCM cows and demonstrated a positive (weak) correlation with SCC. The behavioral comparisons revealed that SCM cows displayed reductions in activity (behavioral transitions and distance moved), social exploration, social reactivity (here: likelihood to be displaced following receipt of agonism), performance of social grooming and head butts, and the receipt of agonistic noncontact challenges. In addition, SCM cows received more head swipes, and spent a greater proportion of time lying with their head on their flank than CTRL cows. The SCM cows also displayed an altered feeding pattern; they spent a greater proportion of feeding time in direct contact with 2 conspecifics, and a lower proportion of feeding time at self-locking feed barriers, than CTRL cows. Behavioral measures were found to correlate, albeit loosely, with serum SAA in a direction consistent with predictions for sickness behavior. These included positive correlations with lying duration and the receipt of all agonistic behavior, and negative correlations with feeding, drinking, the performance of all social and all agonistic behavior, and social reactivity. We conclude that changes in salivary SAA, social behavior, and activity offer potential in the detection of subclinical mastitis and recommend further investigation to substantiate and refine our findings.

Invited review: Mechanisms of hypophagia during disease

Suppression of appetite, or hypophagia, is among the most recognizable effects of disease in livestock, with the potential to impair growth, reproduction, and lactation. The continued evolution of the field of immunology has led to a greater understanding of the immune and endocrine signaling networks underlying this conserved response to disease. Inflammatory mediators, especially including the cytokines tumor necrosis factor-α and interleukin-1β, are likely pivotal to disease-induced hypophagia, based on findings in both rodents and cattle. However, the specific mechanisms linking a cytokine surge to decreased feeding behavior are more difficult to pin down and likely include direct effects on appetite centers in the brain, alteration of gastric motility, and modulation of other endocrine factors that influence appetite and satiety. These insights into the mechanisms for disease-induced hypophagia have great relevance for management of neonatal calves, mature cows transitioning to lactation, and cows experiencing mastitis; however, it is not necessarily the case that increasing feed intake by any means possible will improve health outcomes for diseased cattle. We explore conflicting effects of hypophagia on immune responses, which may be impaired by the lack of specific substrates, versus apparent benefits for controlling the growth of some pathogens. Anti-inflammatory strategies have shown promise for promoting recovery of feed intake following some conditions but not others. Finally, we explore the potential for early disease detection through automated monitoring of feeding behavior and consider which strategies may be implemented to respond to early hypophagia.

Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture

Simple Summary

Monitoring the welfare of cattle and sheep in large pastures can be time-consuming, especially if the animals are scattered over large areas in semi-natural pastures. There are several technologies for monitoring animals with wearable or remote equipment for recording physiological or behavioural parameters and trigger alarms when the acquired information deviates from the normal. Automatic equipment allows continuous monitoring and may give more information than manual monitoring. Ear tags with electronic identification can detect visits to specific points. Collars with positioning (GPS) units can assess the animals’ movements and habitat selection and, to some extent, their health and welfare. Digitally determined virtual fences, instead of the traditional physical ones, have the potential to keep livestock within a predefined area using audio signals in combination with weak electric shocks, although some individuals may have difficulties in responding as intended, potentially resulting in reduced animal welfare. Remote technology such as drones equipped with cameras can be used to count animals, determine their position and study their behaviour. Drones can also herd and move animals. However, the knowledge of the potential effects on animal welfare of digital technology for monitoring and managing grazing livestock is limited, especially regarding drones and virtual fences.

Abstract

The opportunities for natural animal behaviours in pastures imply animal welfare benefits. Nevertheless, monitoring the animals can be challenging. The use of sensors, cameras, positioning equipment and unmanned aerial vehicles in large pastures has the potential to improve animal welfare surveillance. Directly or indirectly, sensors measure environmental factors together with the behaviour and physiological state of the animal, and deviations can trigger alarms for, e.g., disease, heat stress and imminent calving. Electronic positioning includes Radio Frequency Identification (RFID) for the recording of animals at fixed points. Positioning units (GPS) mounted on collars can determine animal movements over large areas, determine their habitat and, somewhat, health and welfare. In combination with other sensors, such units can give information that helps to evaluate the welfare of free-ranging animals. Drones equipped with cameras can also locate and count the animals, as well as herd them. Digitally defined virtual fences can keep animals within a predefined area without the use of physical barriers, relying on acoustic signals and weak electric shocks. Due to individual variations in learning ability, some individuals may be exposed to numerous electric shocks, which might compromise their welfare. More research and development are required, especially regarding the use of drones and virtual fences.

Rumen microbiome structure and metabolites activity in dairy cows with clinical and subclinical mastitis

Background

Due to the high prevalence and complex etiology, bovine mastitis (BM) is one of the most important diseases to compromise dairy cow health and milk quality. The shift in milk compositions has been widely investigated during mastitis, but recent studies suggested that gastrointestinal microorganism also has a crucial effect on the inflammation of other peripheral tissues and organs, including the mammary gland. However, research focused on the variation of rumen inner-environment during mastitis is still limited. Therefore, the ruminal microbial profiles, metabolites, and milk compositions in cows with different udder health conditions were compared in the present study. Furthermore, the correlations between udder health status and ruminal conditions were investigated. Based on the somatic cell counts (SCC), California mastitis test (CMT) parameters and clinical symptoms of mastitis, 60 lactating Holstein dairy cows with similar body conditions (excepted for the udder health condition) were randomly divided into 3 groups (n = 20 per group) including the healthy (H) group, the subclinical mastitis (SM) group and the clinical mastitis (CM) group. Lactation performance and rumen fermentation parameters were recorded. And rumen microbiota and metabolites were also analyzed via 16S rRNA amplicon sequencing and untargeted metabolomics, respectively.

Results

As the degree of mastitis increased, rumen lactic acid (LA) (P < 0.01), acetate, propionate, butyrate, valerate (P < 0.001), and total volatile fatty acids (TVFAs) (P < 0.01) concentrations were significantly decreased. In the rumen of CM cows, the significantly increased bacteria related to intestinal and oral inflammation, such as Lachnospiraceae (FDR-adjusted P = 0.039), Moraxella (FDR-adjusted P = 0.011) and Neisseriaceae (FDR-adjusted P = 0.036), etc., were accompanied by a significant increase in 12-oxo-20-dihydroxy-leukotriene B4 (FDR-adjusted P = 5.97 × 10^− 9) and 10beta-hydroxy-6beta-isobutyrylfuranoeremophilane (FDR-adjusted P = 3.88 × 10^− 10). Meanwhile, in the rumen of SM cows, the Ruminiclostridium_9 (FDR-adjusted P = 0.042) and Enterorhabdus (FDR-adjusted P = 0.043) were increased along with increasing methenamine (FDR-adjusted P = 6.95 × 10^− 6), 5-hydroxymethyl-2-furancarboxaldehyde (5-HMF) (FDR-adjusted P = 2.02 × 10^− 6) and 6-methoxymellein (FDR-adjusted P = 2.57 × 10^− 5). The short-chain fatty acids (SCFAs)-producing bacteria and probiotics in rumen, including Prevoterotoella_1 (FDR-adjusted P = 0.045) and Bifidobacterium (FDR-adjusted P = 0.035), etc., were significantly reduced, with decreasing 2-phenylbutyric acid (2-PBA) (FDR-adjusted P = 4.37 × 10^− 6).

Conclusion

The results indicated that there was a significant shift in the ruminal microflora and metabolites associated with inflammation and immune responses during CM. Moreover, in the rumen of cows affected by SM, the relative abundance of several opportunistic pathogens and the level of metabolites which could produce antibacterial compounds or had a competitive inhibitory effect were all increased.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40104-020-00543-1.

Prepartum behavior changes in dry Holstein cows at risk of postpartum diseases

The objective of this study was to identify changes in prepartum behavior associated with the incidence of postpartum diseases in dairy cows. Multiparous Holstein cows (n = 489) were monitored with accelerometers for 3 wk prepartum. Accelerometers measured steps, time at the feed bunk, frequency of meals, lying bouts, and lying time. Postpartum health was monitored from 0 to 30 d in milk and cases of metritis, mastitis, retained placenta, displaced abomasum (DA), ketosis, and hypocalcemia were recorded. A multivariate linear mixed model was used to assess differences in behavior between diseased and not diagnosed diseased cows. A multivariate logistic regression was used to predict the occurrence of diseases. Predictors were selected using a manual backward stepwise selection process of variables until all remaining predictors had a P < 0.10. Models were submitted to a leave-one-out cross-validation process, and sensitivity, specificity, false discovery rate, and false omission rate were calculated. On average, over the 3-wk prepartum period, cows not diagnosed diseased (n = 345) took 1,613 ± 38 steps, spent 181 ± 7.1 min at the feed bunk, had 8.3 ± 0.17 meals, had 9.8 ± 0.32 lying bouts, and spent 742 ± 11.3 min lying per day. Behavior of diseased cows (n = 144) did not differ from those not diagnosed diseased. However, differences for specific diseases were observed, being significant in the week prepartum. When considering changes in behavior for only the week before calving, cows with metritis had more lying bouts (+21%), cows with DA had fewer meals (-24%) and tended to take fewer steps (-18%), and cows with ketosis had fewer meals (-22%) and spent less time at the feed bunk (-40%). Prediction models with the best outcomes were found for DA and ketosis using data of the prepartum week only. The model for DA included time at the feed bunk. Cross-validation resulted in a 80% sensitivity, 58.1% specificity, 59.2% accuracy, 91.2% false discovery rate, and 1.7% false omission rate. The model for ketosis included time at the feed bunk and number of meals. Cross-validation resulted in 64.3% sensitivity, 59.3% specificity, 59.5% accuracy, 93.0% false discovery rate, and 2.8% false omission rate. Prepartum behavior of cows affected with metritis, DA, and ketosis was different from that of cows not diagnosed with diseases. Prediction equations were able to classify cows at high or low risk of ketosis and DA and can be used in taking management decisions, but the high false discovery rates requires further refinement.

Rumen and Hindgut Bacteria Are Potential Indicators for Mastitis of Mid-Lactating Holstein Dairy Cows

Mastitis is one of the major problems for the productivity of dairy cows and its classifications have usually been based on milk somatic cell counts (SCCs). In this study, we investigated the differences in milk production, rumen fermentation parameters, and diversity and composition of rumen and hindgut bacteria in cows with similar SCCs with the aim to identify whether they can be potential microbial biomarkers to improve the diagnostics of mastitis. A total of 20 dairy cows with SCCs over 500 × 10³ cells/mL in milk but without clinical symptoms of mastitis were selected in this study. Random forest modeling revealed that Erysipelotrichaceae UCG 004 and the [Eubacterium] xylanophilum group in the rumen, as well as the Family XIII AD3011 group and Bacteroides in the hindgut, were the most influential candidates as key bacterial markers for differentiating "true" mastitis from cows with high SCCs. Mastitis statuses of 334 dairy cows were evaluated, and 96 in 101 cows with high SCCs were defined as healthy rather than mastitis according to the rumen bacteria. Our findings suggested that bacteria in the rumen and hindgut can be a new approach and provide an opportunity to reduce common errors in the detection of mastitis.

Proximity Interactions in a Permanently Housed Dairy Herd: Network Structure, Consistency, and Individual Differences

Understanding the herd structure of housed dairy cows has the potential to reveal preferential interactions, detect changes in behavior indicative of illness, and optimize farm management regimes. This study investigated the structure and consistency of the proximity interaction network of a permanently housed commercial dairy herd throughout October 2014, using data collected from a wireless local positioning system. Herd-level networks were determined from sustained proximity interactions (pairs of cows continuously within three meters for 60 s or longer), and assessed for social differentiation, temporal stability, and the influence of individual-level characteristics such as lameness, parity, and days in milk. We determined the level of inter-individual variation in proximity interactions across the full barn housing, and for specific functional zones within it (feeding, non-feeding). The observed networks were highly connected and temporally varied, with significant preferential assortment, and inter-individual variation in daily interactions in the non-feeding zone. We found no clear social assortment by lameness, parity, or days in milk. Our study demonstrates the potential benefits of automated tracking technology to monitor the proximity interactions of individual animals within large, commercially relevant groups of livestock.

Population physiologically based modeling of pirlimycin milk concentrations in dairy cows

Predictions of drug residues in milk are critical in food protection and are a major consideration in the economics of treatment of mastitis in dairy cows. Nonlinear mixed-effects modeling (NLME) has been advocated as a suitable pharmaco-statistical method for the study of drug residues in milk. Recent developments in physiologically based pharmacokinetic (PBPK) modeling of intramammary drugs allow the combination of a mechanistic description of milk pharmacokinetics with NLME methods. The PBPK model was applied to NLME analysis of a data set consisting of milk drug concentrations from 78 healthy cows and 117 with clinical mastitis. Pirlimycin milk pharmacokinetics were adequately described by the model across the range of observed concentrations. Mastitis was characterized by increased variance in milk production volume. Udder residual volume was larger in cows with 1, or 2 or greater diseased mammary glands than in the healthy cows. Low-producing cows had a greater risk of prolonged milk residues. With the exclusion of the low-production cows, the model predicted that healthy cows required a milk discard time 12 h longer than that indicated by the label, and the diseased cows 36 h longer than indicated by the label. More pirlimycin was systemically absorbed in the gram-positive infected compared with the gram-negative infected or healthy cows, suggesting a greater risk of violative meat residues in gram-positive infected cows. Using NLME and PBPK models, we identified factors associated with changes in pirlimycin milk residues that may affect food safety. This model extends the verification of a simple physiologically based framework for the study of intramammary drugs.

Behavioral changes associated with fever in transition dairy cows

Dairy cows are often diagnosed with fever without showing clinical symptoms of disease. The aim of this study was to investigate changes in feeding, social, and lying behaviors of cows with fever but without clinical disease, as compared with healthy cows. After parturition, dairy cows of mixed parities were housed in a dynamic group of 20. In the freestall pen, cows had access to 12 electronic feed bins, 2 electronic water bins, and 24 lying stalls. Feeding and social behaviors were recorded using the electronic feed bins, and lying behaviors were measured using electronic data loggers attached to the cow. Rectal body temperature was assessed on a daily basis, and fever defined as a body temperature >39.5°C. All cows were examined for metritis every third day after calving, and all other diseases (e.g., mastitis, ketosis) were diagnosed as per farm protocol. Cows with multiple days of fever (n = 8) and cows with 1 d of fever (n = 18) that were not diagnosed with a clinical disease were compared with a matched sample of healthy cows (i.e., cows that were not clinically ill and never had a fever recorded) of the same parity (categorized as primiparous vs. multiparous). Feeding, social, and lying behaviors were compared for the first 2 d of fever in cows with multiple days of fever, and the day of fever in cows with 1 d of fever. Cows of both fever groups spent less time feeding compared with controls (135 vs. 181 ± 7.6 min/d for multiple fever days, and 158 vs. 185 ± 9.7 min/d for 1 d of fever). Cows with 1 d of fever ate at a faster rate (109 vs. 91 ± 5 g/min) and had a lower number of replacements at the feed bunk (actor replacements: 9.7 vs. 14.6 ± 1.7 no./d; reactor replacements: 11.1 vs. 15.9 ± 1.6 no./d) compared with healthy controls. Overall, cows with fever showed behavioral changes such as decreased feeding time that are consistent with sickness responses described in other species.

Predicting Disease in Transition Dairy Cattle Based on Behaviors Measured Before Calving

Simple Summary

Dairy cattle often become ill after calving. We developed models designed to predict which cows are likely to become ill based upon measures of the cows’ feeding and competitive behaviors before calving. Our models had high sensitivity (73–71%), specificity (80–84%), positive predictive values (73–77%), and negative predictive values (80–80%) for both cows that had previously calved and for those calving for the first time. We conclude that behaviors at the feed bunk before calving can predict cows at risk of becoming sick in the weeks after calving.

Abstract

Dairy cattle are particularly susceptible to metritis, hyperketonemia (HYK), and mastitis in the weeks after calving. These high-prevalence transition diseases adversely affect animal welfare, milk production, and profitability. Our aim was to use prepartum behavior to predict which cows have an increased risk of developing these conditions after calving. The behavior of 213 multiparous and 105 primiparous Holsteins was recorded for approximately three weeks before calving by an electronic feeding system. Cows were also monitored for signs of metritis, HYK, and mastitis in the weeks after calving. The data were split using a stratified random method: we used 70% of our data (hereafter referred to as the “training” dataset) to develop the model and the remaining 30% of data (i.e., the “test” dataset) to assess the model’s predictive ability. Separate models were developed for primiparous and multiparous animals. The area under the receiver operating characteristic (ROC) curve using the test dataset for multiparous cows was 0.83, sensitivity and specificity were 73% and 80%, positive predictive value (PPV) was 73%, and negative predictive value (NPV) was 80%. The area under the ROC curve using the test dataset for primiparous cows was 0.86, sensitivity and specificity were 71% and 84%, PPV was 77%, and NPV was 80%. We conclude that prepartum behavior can be used to predict cows at risk of metritis, HYK, and mastitis after calving.

Competition Strategies of Metritic and Healthy Transition Cows

Simple Summary

Competition for feed is a social stressor for dairy cows and is associated with an increased risk of illness. We investigated how cows trade off the motivation to feed together with group mates against the risk of competitive interactions at the feeder, and in this way identified each individual’s competition strategy. We then related these strategies to cow health. Competition strategies varied between cows and showed low to moderate stability over time. Strategies of metritic and healthy cows did not differ before or after calving, but metritic cows changed strategies more upon entering the social group after calving, particularly in the days before diagnosis. We conclude that cows show individual competition strategies, and that automated measures of strategy change may help in detecting metritis.

Abstract

Our study aimed to characterize social competition strategies in transition cows, and determine how these varied with health status. We retrospectively followed 52 cows during 3 periods (PRE: d −6 to −1 prepartum, POST1: d 1 to 3 postpartum, POST2: d 4 to 6 postpartum). Cows diagnosed with metritis on d 6 postpartum (n = 26) were match paired with healthy cows (n = 26). Measures of agonistic behavior (i.e., replacements at the feeder) and feeding synchrony were determined by an algorithm based on electronic feed bin data, and used to calculate competition strategies via principal component analysis. We found consistent strategies, defined by two components (asynchrony and competitiveness; explaining 82% of the total variance). We observed no differences in strategies when comparing healthy and metritic cows, but metritic cows tended to change their strategies more between PRE and POST1, and between POST1 and POST2, indicating that strategies change in association with parturition and metritis. We conclude that cows show individual variation in competition strategies, and that automated measures of strategy change may help in detecting metritis.

Dairy cows with mild-moderate mastitis change lying behavior in hospital pens

In dairy production, mastitis is a major problem affecting animal welfare, productivity, and economy. Hospital pens are typically not used for cows with mastitis, except for severe cases involving recumbency. This field trial included 47 cows from three Danish herds followed for 8 d, of which days 1–5 involved the experimental housing. After day 5, all cows were kept with the lactating group. We examined lying behavior in dairy cows with naturally occurring, mild-moderate mastitis in hospital pens [single or group (depending on conditions on the farm), all with deep straw bedding] vs. sick cows kept in the group of healthy herd mates. Within a herd, every other cow fulfilling the inclusion criteria regarding mastitis was allocated to each of the two experimental treatments. Clinical data from involved cases were collected. No significant differences between housing treatments were found in the clinical variables or the daily lying time. During the period of experimental housing, cows kept in hospital pens showed a higher frequency of lying bouts compared with control cows. This difference did not persist after reintroduction to the lactating herd mates. These results suggest that aspects of lying behavior of dairy cows with mastitis are sensitive to the environment as the frequency of lying bouts differed between cows kept in hospital pens and cows kept in control treatment. More controlled studies are needed to examine underlying motivations and evaluate consequences in terms of animal welfare. For such studies, the inclusion of healthy cows for comparison will be valuable.

Alterations in sick dairy cows' daily behavioural patterns

The recent development of dairy production is characterised by increasing herd sizes and therefore increasingly complicated visual observation of cow behaviour, which is traditionally the basis for diagnoses of production diseases. The limitation of the direct visual behavioural observation due to the increasing number of individual cows implies a growing need for an automated detection of changes within behavioural patterns to identify cows that show sickness behaviour. Sensor systems can be used to measure behavioural patterns such as activity, resting, feeding and rumination. Behavioural patterns change with the occurrence of sickness but also interact with external factors. Changes such as prolonged lying duration or shortened feeding duration caused by metabolic disorders or infections, respectively, then serve as a detection tool for sick individuals. The aim of the present review is to outline the impact of production diseases on the daily behavioural patterns of dairy cows by referring to sickness behaviour.

Characterization of Microbial Communities in a Dairy Farm Matrix in Ningxia, China, by 16S rDNA Analysis

A large amount of dairy manure is produced annually in the Ningxia Hui Autonomous Region of China due to the increase in food-producing animal agriculture in this region. The presence of bovine-originated zoonotic, especially human, pathogenic bacteria in untreated manure poses a significant threat to the environment and to public health. However, little is known about the composition, diversity, and abundance of bacterial communities in untreated dairy manure in the Ningxia region. In this study, the microbial community structure of the dairy farm matrix was characterized through 16S rDNA sequencing. The impact of manure treatment methods on bacterial communities was also analyzed. The results showed that the microbial community in dairy manure contained both beneficial bacteria and pathogens, with Firmicutes, Bacteroidetes, Proteobacteria, Spirochaetes, and Actinobacteria as dominant phyla. The results also showed the diversity and variety of abundance of zoonotic pathogens among different matrices. The number of pathogens was found to increase significantly in the accumulated but untreated manure, which appeared to be the main matrix of dairy farms that accumulated pathogens including zoonotic pathogens. Findings from this study suggested that farm management, particularly proper treatment of manure, is essential to achieve a shift in the bacterial community composition and a reduction in the environmental load of pathogens including zoonotic pathogens.

Infectious Disease Does Not Impact the Lying and Grooming Behaviour of Post-Parturient Dairy Cows

Simple Summary

Animals reduce their activity, feeding behaviour, social interactions and self-grooming behaviours when sick (‘sickness behaviours’). However, the effect of sickness on early maternal behaviours directed toward offspring is less understood, especially in farm animals. This experiment assessed the impact of sickness on the first day after giving birth on the lying and grooming behaviour of dairy cows and their calves. The behaviours of sick and healthy cows and their calves were recorded for 24 hours after calving. Behaviours included the lying behaviour of the cow and calf, and maternal grooming of the calf. We found that health status did not affect the majority of the behaviours measured after calving. We suggest that a cow’s motivation to groom and be near their calf may be stronger than her motivation to express sickness behaviours immediately after giving birth.

Abstract

Behaviour is commonly used to detect sickness in animals, but the impact of sickness on lying and maternal behaviours around parturition is not well understood. The objective was to assess the effects of sickness on the lying and grooming behaviours of dairy cows in the first 24 h after giving birth. Cows were categorized as ‘sick’ (n = 8) if they had at least one rectal temperature ≥39.1 °C and one clinical sign of illness (mastitis, pneumonia or an unknown infection) within 24 h of calving. These cows were match-paired for parity with cows that had no rectal temperature ≥39.1 °C and no clinical signs of illness up to 3 d after calving (n = 8; ‘not sick’). The duration and latency of cow behaviours (standing, lying, lying bouts, lying close to calf, and grooming of the calf) and calf behaviours (standing and lying) were recorded for 24 h post-partum. We found no differences in the behaviour of sick and not sick cows and their calves post-calving, except that sick cows took longer to lie down near their calf after calving compared to those without illness. Cows may be more motivated to groom and spend time with their calf than to express sickness behaviours immediately after giving birth.

Automatic detection of feeding- and drinking-related agonistic behavior and dominance in dairy cows

Accurate assessments of social behavior and dominance relationships in cattle can be time consuming. We investigated whether replacements at the feed bunk and water trough-one type of agonistic interaction-can be used to automatically assess dominance relationships. Our study set out to (1) validate a replacement detection algorithm using combined data from electronic feed and water bins, and (2) investigate the applicability of this algorithm to identify individual dominance scores and group-level social hierarchy in freestall-housed dairy cows. We used 4 groups of lactating cows kept in different group sizes (11 to 20 cows) located at 2 research facilities. In both facilities, feed and water were provided via automated feeding systems. A trained observer recorded all agonistic interactions in the pen over multiple days using video. Data from the electronic feed and water bins for the same days were analyzed using an algorithm to detect replacements (i.e., visits where a receiver cow was competitively replaced by an actor cow). Most agonistic interactions at the feed bunk were replacements. These replacements were associated with a brief interval between the time the receiver cow left the bin and the actor cow took her place; the optimal threshold to detect these replacements varied from 22 to 27 s between groups, independent of stocking density. The recall and precision of an algorithm based upon this threshold was high (on average >0.8), comparable to that of trained human observers. We improved data preparation by controlling for detection errors and included filtering to reduce false positives. This resulted in a >20% decrease in false positives and an increase in precision of 0.043. The dominance hierarchy based upon algorithm-detected replacements was similar to that based upon total agonistic interactions observed in the pen; the Spearman rank correlation coefficient between these hierarchies varied among the groups from 0.81 to 0.96. We conclude that data from electronic feed and water bins can accurately estimate agonistic behavior and dominance relationships among dairy cows.

Effects of nematode parasitism on activity patterns in first-season grazing cattle

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Effects of GIN was evaluated for the first time in FSG using accelerometers.

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GIN affect activity patterns in FSG cattle on grass.

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Discrepancies from classic sickness behaviour may indicate discomfort.

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There is a potential use of automated behavioural observations as a diagnostic tool.

We investigated the effects of gastrointestinal nematode (GIN) challenge on activity patterns in first season grazing (FSG) steers exposed to two different levels of Ostertagia ostertagi and Cooperia oncophora. At turn-out, experimental animals were allocated to one of two treatment groups grazing in different enclosures each with 32 animals. The first group (High) received 5000 third stage (L3) O. ostertagi (50%) and C. onchophora (50%) larvae; whereas the second group (Low) were dewormed monthly with 0.5 mg ivermectin (Noromectin®, Pour-on) per kg bodyweight. Activity patterns were monitored by fitting some animals in each group (High, n = 10; Low, n = 8) with leg mounted sensors (IceTag® 3D-accelerometers) during three two-week periods. In animals fitted with sensors body weight gain (BWG) was recorded every fortnight, whereas faecal and blood samples were collected every four weeks for nematode faecal egg count (FEC) and serum pepsinogen concentrations (SPC). Differences between the periods in daily (P =  0.046) and diurnal (P =  0.0502) activities were recorded between groups during the course of the study. A significant (P =  0.038) increase in the number of lying bouts was recorded in group High during the second period (days 74–86), which was correlated (r = 0.55, P = 0.018) to an increase in SPC ≈ 85 days after turn-out. BWG was reduced (P =  0.037) in group High compared to group Low, deviating from day 45. Strongyle nematode eggs were observed in both groups 29 days after turn-out, however the mean EPG remained low in group Low throughout the experiment. An increase in SPC was observed (P <  0.0038) in group High with levels peaking on day 58. In conclusion, our data supports that changes in activity patterns monitored with sensors could contribute to the identification of animals challenged with GIN, but also improve our understanding in the potential welfare impairments caused by such infections.

An Update on the Effect of Clinical Mastitis on the Welfare of Dairy Cows and Potential Therapies

Despite the widespread implementation of mastitis control programs, mastitis is the most common and one of the costliest diseases in the dairy industry, with broad-ranging impacts and consequences. Recent technological advances have allowed researchers to assess the effects of mastitis on animal behavior and welfare, and the efficacy of mastitis treatments. Several nonsteroidal anti-inflammatory drugs are available as supportive therapies for clinical mastitis. This article focuses on recent advances in the assessment, therapy, and effects of mastitis on cow behavior and welfare.

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

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

Influence of previous medical treatments on social rank in dairy cows

Animal health can present economic and animal welfare concerns for dairy farmers. However, it is unknown whether changes in behaviour as a result of sickness have long-term effects, and whether there is a relationship between the social rank of cows in the herd at a moment in time and the medical treatment history of these cows. The behaviour of 100 high-yielding cows in the waiting area (collecting yard) before the milking parlour was studied for five milking sessions to assess the interactions between the cows, as the cows waited to be milked. The cows were filmed, and the interactions between cows were recorded using an ethogram. The summated social rank score of each cow was compared with its medical treatment history. In our analysis, a positive relationship was found between medical treatment history and social rank, however this association was discounted when age was accounted for. When a subsample of cows older than 4.0 years was analysed to examine the relationship between social rank and medical treatment history in older cows, no significant correlation existed. These results suggest that the relationship between social rank and medical treatment history identified in the analysis is predominately an effect of age. This finding is contradictory to the hypothesis that ‘a higher historical level of medical treatments, and the disruption and setbacks associated with these disease conditions, would result in a lower position in the social rank.’ This preliminary study reports the findings from one particular herd, and across one relatively short time span, and because associations between treatment history and behaviour may be of interest in management decisions for producers, it is recommended that further investigation of this subject is carried out.

Diagnosis of bovine mastitis: from laboratory to farm

Accurate diagnosis of disease is the major step between the cause and cure of disease. An economical, reliable, and rapid diagnostic tool is fundamental for the management of udder health. The earlier the disease is identified, the less will be the damage; keeping this in mind, many efforts are being made to develop reliable diagnostic tools for use on farm. However, traditional gold standard methods including somatic cell count and microbial culturing are still in use. They are partially being replaced with polymerase chain reaction and sequencing-based tests. Nanotechnology and protein-based tests have also gained lot of attention and some of them are potential candidate of future diagnostic tests for bovine mastitis. Research laboratories are struggling to develop simple, economical, and user-friendly biosensor-based methods that can be performed on farm for rapid diagnosis. The combination of both genomic and proteomic approaches, together with further involvement of nanotheranostic technologies and other sensors, will assist in the quest of better mastitis diagnostic tools.

Short communication: Detection and monitoring of metritis in dairy cows using an automated grooming device

Metritis, a prevalent disease on dairy farms, is negatively associated with reproduction, milk production, and the welfare of cows. The objective of this study was to evaluate the efficacy of monitoring low-resilience activities (i.e., behaviors that typically decrease when energy resources are limited or when the cost involved in the activity increases; e.g., brush usage) in the early detection of metritis. Data on daily brush usage (i.e., proportion of cows using the brush and the duration of usage) were collected from 28 metritic and 60 control cows 28 d postpartum using an automated monitoring system developed for the purpose of this study. During the first week following partum (before clinical diagnosis), we found no differences in brush usage between sick and control cows. However, 8 to 21 d postpartum (the week of clinical diagnosis and the first week of medical treatment), a lower proportion of metritic cows used the brush compared with control cows (0.49 compared with 0.64, respectively, at brushes installed away from the feed bunk). In addition, the daily duration of brush usage was 50% lower among cows diagnosed with metritis compared with control cows 8 to 28 d postpartum (44 s/d compared with 88 s/d, respectively). The results of this study suggest that on-farm monitoring of low-resilience behaviors, combined with existing systems that monitor core behaviors (e.g., activity and rumination), may serve as an improved method for detecting events that compromise the welfare of animals. The slow recovery of low-resilience behaviors following medical treatment (wk 4) might serve as a particularly useful indicator of progress of recovery from disease.

Intramammary infusion of Escherichia coli lipopolysaccharide negatively affects feed intake, chewing, and clinical variables, but some effects are stronger in cows experiencing subacute rumen acidosis

Feeding high-grain diets increases the risk of subacute rumen acidosis (SARA) and adversely affects rumen health. This condition might impair the responsiveness of cows when they are exposed to external infectious stimuli such as lipopolysaccharide (LPS). The main objective of this study was to evaluate various responses to intramammary LPS infusion in healthy dairy cows and those experimentally subjected to SARA. Eighteen early-lactating Simmental cows were subjected to SARA (n = 12) or control (CON; n = 6) feeding conditions. Cows of the control group received a diet containing 40% concentrates (DM basis) throughout the experiment. The intermittent SARA feeding regimen consisted in feeding the cows a ration with 60% concentrate (DM basis) for 32 d, consisting of a first SARA induction for 8 d, switched to the CON diet for 7 d, and re-induction during the last 17 d. On d 30 of the experiment, 6 SARA (SARA-LPS) and 6 CON (CON-LPS) cows were intramammary challenged once with a single dose of 50 μg of LPS from Escherichia coli (O26:B6), whereas the other 6 SARA cows (SARA-PLA) received 10 mL of sterile saline solution as placebo. To confirm the induction of SARA, the reticular pH was continuously monitored via wireless pH probes. The DMI remained unchanged between SARA and CON cows during the feeding experiment, but was reduced in both treatment groups receiving the LPS infusion compared with SARA-PLA, whereby a significant decline was observed for cows of the SARA-LPS treatment (-38%) compared with CON-LPS (-19%). The LPS infusion did not affect the reticuloruminal pH dynamics, but significantly enhanced ruminal temperature and negatively affected chewing behavior. The ruminal temperature increased after the LPS infusion and peaked about 1 h earlier in SARA-LPS cows compared with the cows of the CON-LPS treatment. Moreover, a significant decline in milk yield was found in SARA-LPS compared with CON-LPS following the LPS infusion. Cows receiving LPS had elevated somatic cell counts, protein, and fat contents in milk as well as decreased lactose contents and pH following the LPS infusion, whereby the changes in milk constituents were more pronounced in SARA-LPS than CON-LPS cows. Rectal temperature and pulse rate were highest 6 h after LPS infusion, but rumen contractions were not affected by the LPS infusion. The data suggest that a single intramammary LPS infusion induced fever and negatively affected feed intake, chewing activity, rectal temperature, and milk yield and composition, whereby these effects were more pronounced in SARA cows.