Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part I. Metabolic and digestive disorders

Patterns of periparturient rumination and activity time in multiparous Holstein cows with and without dyscalcemia in early lactation

Dyscalcemia, defined as reduced blood Ca at 4 DIM, is associated with reduced milk production and reproduction and an increased risk of negative health events. Cowside testing of blood Ca to diagnose dyscalcemia is difficult, and alternative methods to identify dyscalcemia are needed. Our objectives were to explore differences in periparturient rumination and activity time between cows with and without dyscalcemia and use activity and rumination variables to identify dyscalcemia. We performed a retrospective cohort analysis on data collected from multiparous Holstein cows (n = 182) from 2 herds in New York. Cows were affixed with ear or neck loggers to record daily activity (arbitrary units [AU]/d, defined by manufacturer) and rumination (min/d) times. Daily activity and rumination times were collected from 14 d before calving until 14 DIM. No cows received supplemental calcium or experienced clinical hypocalcemia during the study period. A blood sample was collected at 4 DIM and analyzed for total calcium concentration, and cows were subsequently classified as dyscalcemic if total calcium was ≤2.2 mmol/L (n = 57) or eucalcemic if total calcium was >2.2 mmol/L (n = 125). Linear mixed models were used to analyze differences in pre- and postpartum activity and rumination times between the calcemic groups. Logistic regression models were used to identify the probability of dyscalcemia from activity and rumination time variables from 0 to 4 DIM. Prepartum activity time was similar between eucalcemic and dyscalcemic cows (402.0 ± 10.4 AU/d and 395.1 ± 14.5 AU/d, respectively). Postpartum eucalcemic cows had greater activity time than dyscalcemic cows (436.1 ± 10.7 vs. 407.8 ± 14.7 AU/d, respectively). Prepartum rumination time was similar between eucalcemic and dyscalcemic cows (512.6 ± 9.8 min/d vs. 504.2 ± 14 min/d, respectively). Postpartum eucalcemic cows had greater rumination time than dyscalcemic cows (512.3 ± 10.5 min/d vs. 480.5 ± 15 min/d, respectively). Logistic regression models yielded AUC values ranging from 0.71 to 0.79, sensitivities of 17.5% to 40.3%, specificities of 91.2% to 94.4%, accuracy of 70.3% to 77.0%, positive predictive values of 59.0 to 76.0%, and negative predictive values of 72.0% to 78.0%. Our findings suggest that differences exist in postpartum activity and rumination times between cows that experience dyscalcemia and those that remain eucalcemic. Utilizing activity and rumination time data in the immediate postpartum period shows utility in identifying cows with dyscalcemia, which could aid in management decisions that ameliorate the associated negative outcomes; however, further work is needed to optimize their capabilities.

Reproductive physiological outcomes of dairy cows with different genomic merit for fertility: biomarkers, uterine health, endocrine status, estrus features, and response to ovarian synchronization

Our overarching objective was to characterize associations between genomic merit for fertility and the reproductive function of lactating dairy cows in a prospective cohort study. In this manuscript, we present results of the association between genomic merit for fertility and indicators of metabolic status and inflammation, uterine health, endocrine status, response to synchronization, and estrous behavior in dairy cows. Lactating Holstein cows entering their first (n = 82) or second (n = 37) lactation were enrolled at parturition and fitted with an ear-attached sensor for automated detection of estrus. Ear-notch tissue samples were collected from all cows and submitted for genotyping using a commercial genomic test. Based on genomic predicted transmitting ability values for daughter pregnancy rate (gDPR) cows were classified into a high (Hi-Fert; gDPR > 0.6; n = 36), medium (Med-Fert; gDPR -1.3 to 0.6; n = 45), and low (Lo-Fert; gDPR < -1.3; n = 38) group. At 33 to 39 d in milk (DIM), cohorts of cows were enrolled in the Presynch-Ovsynch protocol for synchronization of estrus and ovulation. Body weights, body condition scores (BCS), and uterine health measurements (i.e., vaginal discharge, uterine cytology) were collected from parturition to 60 DIM and milk yield was collected through 90 DIM. Blood samples were collected weekly through 3 wk of lactation for analysis of β-hydroxybutyrate, nonesterified fatty acids, and haptoglobin plasma concentrations. Body weight, BCS, NEFA, BHB, and Haptoglobin were not associated with fertility groups from 1 to 9 wk after parturition. The proportion of cows classified as having endometritis at 33 to 36 DIM tended to be greater for the Lo-Fert than the Hi-Fert group. The proportion of cows that resumed cyclicity did not differ at any time point evaluated and there were no significant associations between probability or duration and intensity of estrus with fertility group. Cows of superior genetic merit for fertility were more likely to ovulate, have a functional CL, have greater circulating P4, and have larger ovulatory size than cows of inferior fertility potential at key time points during synchronization of estrus and ovulation. Despite observing numerical differences with potential performance consequences for the proportion of cows that responded to synchronization of ovulation and were both cyclic and responded to the Ovsynch portion of the synchronization protocol, we did not observe significant differences between fertility groups. Although not consistent and modest in magnitude, the collective physiological and endocrine differences observed suggested that cows of superior genetic fertility potential might have improved reproductive performance, at least in part, because of modestly improved endocrine status, uterine health, and ability to ovulate.

Use of rumination time in health risk assessment of prepartum dairy cows

The objectives of this observational cohort study were to evaluate the associations of rumination time (RT) in the last week of pregnancy with transition cow metabolism, inflammation, health, and subsequent milk production, reproduction, and culling. Pregnant nulliparous (n = 199) and parous (n = 337) cows were enrolled 21 d before the expected calving. RT and physical activity were monitored automatically by sensors from d -21 to 15 relative to calving. Blood samples were collected on d -14, -5, 4, 8, and 12 ± 1 relative to calving. Diagnoses of clinical health problems were performed by researchers from calving to 15 d in milk (DIM). In classification 1, cows were ranked based on average daily RT in the last week of pregnancy and classified into terciles as short RT (SRT), moderate RT (MRT), or long RT (LRT) for association analyses. In classification 2, RT deviation from the parity average was used in a receiver operating characteristic curve to identify the best threshold to predict postpartum clinical disease. Cows were then classified as above the threshold (AT) or below the threshold (BT). Compared with cows with LRT, cows with SRT had greater serum concentrations of NEFA (0.47 vs 0.40 ± 0.01 mmol/L), BHB (0.58 vs 0.52 ± 0.01 mmol/L), and haptoglobin (0.22 vs 0.18 ± 0.008 g/L) throughout the transition period, and reduced concentrations of glucose, cholesterol, albumin, and magnesium in a time-dependent manner. Parous cows with SRT had higher odds of postpartum clinical disease (adjusted odds ratio [AOR]: 3.7; 95% confidence interval [CI]: 2.1-6.4), lower odds of pregnancy by 210 DIM (AOR: 0.34; CI: 0.15-0.75), and lower milk production (46.9 vs 48.6 ± 0.5 kg/d) than parous cows with LRT. Deviation in prepartum RT had good predictive value for clinical disease in parous cows (area under the curve [AUC]: 0.65; CI: 0.60-0.71) but not in nulliparous (AUC: 0.51; CI: 0.42-0.59). Separation of parous cows according to the identified threshold (≤-53 min from the parity average) resulted in differences in serum concentrations of NEFA (AT = 0.31 ± 0.006, BT = 0.38 ± 0.014 mmol/L), BHB (AT = 0.49 ± 0.008, BT = 0.53 ± 0.015 mmol/L), and globulin (AT = 32.3 ± 0.3, BT = 34.8 ± 0.5 g/L) throughout the transition period, as well as in serum cholesterol, urea, magnesium, albumin, and haptoglobin in a time-dependent manner. BT parous cows had higher odds of clinical disease (AOR: 3.7; CI: 2.1-6.4), reduced hazard of pregnancy (AHR: 0.64, CI: 0.47-0.89), greater hazard of culling (AHR: 2.1, CI: 1.2-3.6), and lower milk production (46.3 ± 0.7 vs 48.5 ± 0.3 kg/d). External validation using data from 153 parous cows from a different herd and the established threshold in RT deviation (≤-53 min) resulted in similar predictive value, with the odds of postpartum disease 2.4 times greater in BT than AT (37.5 vs 20.1%). In conclusion, RT in the wk preceding calving was a reasonable predictor of postpartum health and future milk production, reproduction, and culling in parous cows but not in nulliparous cows.

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.

Predictive modelling of deviation from expected milk yield in transition cows on automatic milking systems

The transition period is a pivotal time in the production cycle of the dairy cow. It is estimated that between 30% and 50% of all cows experience metabolic or infectious disease during this time. One of the most common and economically consequential effects of disease during the transition period is a reduction in early lactation milk production. This has led to the utilisation of deviation from expected milk yield in early lactation as a proxy measure for transition health. However, to date, this analysis has been used exclusively for the retrospective assessment of transition cow health. Statistical models capable of predicting deviations from expected milk yield may allow producers to proactively manage animals predicted to suffer negative deviations in early lactation milk production. The objective of this retrospective cohort study was first, to explore the accuracy with which cow-level production and behaviour data collected on automatic milking systems (AMS) from 1-3 days in milk (DIM) can predict deviation from expected 30-day cumulative milk yield in multiparous cows. And second, to assess the accuracy with which predicted yield deviations can classify cows into groups which may facilitate improved transition management. Production, rumination, and physical activity data from 31 commercial AMS were accessed. A 3-step analytical procedure was then conducted. In Step 1, expected cumulative yield for 1-30 DIM for each individual cow-lactation was calculated using a mixed effect linear model. In Step 2, 30-Day Yield Deviation (YD) was calculated as the difference between observed and expected cumulative yield. Lactations were then assigned to one of three groups based on their YD, RED Group (0% YD). In Step 3, yield, rumination, and physical activity data from days 1-3 in lactation were used to predict YD using machine learning models. Following external validation, YD was predicted across the test data set with a mean absolute error of 9%. Categorisation of animals suffering large negative deviations (RED group) was achieved with a specificity of 99%, sensitivity of 35%, and balanced accuracy of 67%. Our results suggest that milk yield, rumination and physical activity patterns expressed by dairy cows from 1-3 DIM have utility in the prediction of deviation from expected 30-day cumulative yield. However, these predictions currently lack the sensitivity required to classify cows reliably and completely into groups which may facilitate improved transition cow management.

Evaluation of sensor-based health monitoring in dairy cows: Exploiting rumination times for health alerts around parturition

The use of sensor-based measures of rumination time as a parameter for early disease detection has received significant attention in scientific research. This study aimed to assess the accuracy of health alerts triggered by a sensor-based accelerometer system within 2 different management strategies on a commercial dairy farm. Multiparous Holstein cows were enrolled during the dry-off period and randomly allocated to conventional (CON) or sensor-based (SEN) management groups at calving. All cows were monitored for disorders for a minimum of 10 DIM following standardized operating procedures (SOPs). The CON group (n = 199) followed an established monitoring protocol on the farm. The health alerts of this group were not available during the study but were later included in the analysis. The SEN group (n = 197) was only investigated when the sensor system triggered a health alert, and a more intensive monitoring approach according to the SOPs was implemented. To analyze the efficiency of the health alerts in detecting disorders, the sensitivity (SE) and specificity (SP) of health alerts were determined for the CON group. In addition, all cows were divided into 3 subgroups based on the status of the health alerts and their health status, to retrospectively compare the course of rumination time. Most health alerts (87%, n = 217) occurred on DIM 1. For the confirmation of diagnoses, health alerts showed SE and SP levels of 71% and 47% for CON cows. In SEN cows, a SE of 71% and 75% and SP of 48% and 43% were found for the detection of ketosis and hypocalcemia, respectively. The rumination time of the subgroups was affected by DIM and the interaction between DIM and the status of health alert and health condition.

The Prediction of Clinical Mastitis in Dairy Cows Based on Milk Yield, Rumination Time, and Milk Electrical Conductivity Using Machine Learning Algorithms

In commercial dairy farms, mastitis is associated with increased antimicrobial use and associated resistance, which may affect milk production. This study aimed to develop sensor-based prediction models for naturally occurring clinical bovine mastitis using nine machine learning algorithms with data from 447 mastitic and 2146 healthy cows obtained from five commercial farms in Northeast China. The variables were related to daily activity, rumination time, and daily milk yield of cows, as well as milk electrical conductivity. Both Z-standardized and non-standardized datasets pertaining to four specific stages of lactation were used to train and test prediction models. For all four subgroups, the Z-standardized dataset yielded better results than those of the non-standardized one, with the multilayer artificial neural net algorithm showing the best performance. Variables of importance had a similar rank in this algorithm, indicating the consistency of these variables as predictors for bovine mastitis in commercial farms with similar automatic systems. Moreover, the peak milk yield (PMY) of mastitic cows was significantly higher than that of healthy cows (p < 0.005), indicating that high-yielding cattle are more prone to mastitis. Our results show that machine learning algorithms are effective tools for predicting mastitis in dairy cows for immediate intervention and management in commercial farms.

Associations among post-partum rumen fill and motility, subclinical ketosis and fertility in Holstein dairy cows

This prospective observational study aimed to investigate the association of rumen fill and motility in post-partum Holstein cows with their future reproductive performance and subclinical ketosis (SCK). The study population consisted of two independent data sets: the first (DS1) included 237 cows from 6 herds and the second one (DS2) 709 cows from 9 herds. Rumen Fill Score (RFS) was transformed into a 3 level-trait, representing very low, low and adequate dry matter intake, respectively. A binary Rumen Contraction Score (RCS) was defined as: 0: <2 contractions/2 min, impaired rumen motility and 1: ≥2 contractions/2 min, normal rumen motility. A combined binary trait based on RFS and RCS (RFCS) was also established, representing unsatisfactory and satisfactory rumen function. Three SCK traits were defined, based on 3 different thresholds, SCK_I: BHB≥1,000 mmol/L, SCK_II: BHB≥1,100 mmol/L and SCK_III: BHB≥1,200 mmol/L. Scores were assessed and blood samples collected on day 7 (DS1) or day 8 (DS2), postpartum. Kaplan-Meier survival analysis, multivariable Cox proportional hazards models and Generalized Linear Mixed Models were performed to evaluate the association of rumen and SCK traits with reproduction. Herd, parity, calving season and several postparturient diseases were also included as potential explanatory variables. Mean days from calving to pregnancy after the 1st artificial insemination (AI) and from calving to pregnancy (all AIs) were shorter for levels of rumen traits representing adequate DMI and normal rumen motility; in most cases these differences were statistically significant in both datasets. Cows with adequate DMI and normal rumen motility (only in DS2) had greater hazard (hazard ratio [HR] = 1.84 and 1.61, for RFS and RFCS, respectively) and odds (odds ratio [OR] = 2.49 and 1.98, for RFS and RFCS, respectively) for pregnancy at 1st AI. Assessment of the association of examined rumen traits with hazard and odds for pregnancy at all AIs yielded statistically significant results in both datasets. For RFS, RCS and RFCS, HRs ranged from 1.57 to 3.31 and ORs from 1.95 to 4.83. No statistically significant associations with hazard and odds for pregnancy at 1st or all AIs were detected, for any of the 3 SCK traits, in either dataset. Overall, the combined RFCS trait constantly identified more than twice the number of cows with future reproductive problems than a positive SCK blood test.

Effects of targeted clinical examination based on alerts from automated health monitoring systems on herd health and performance of lactating dairy cows

Our objectives were to compare the proportion of lactating dairy cows diagnosed with health disorders (HD) and herd performance when using a health monitoring program designed to rely primarily but not exclusively on alerts from automated health monitoring (AHM) systems or a health monitoring program based primarily on systematic clinical examinations, milk yield monitoring, and visual observation of cows. In a clinical trial, at ∼30 d before expected parturition, nulliparous and parous Holstein cows, stratified by parity and days in gestation, were randomly assigned to the high-intensity clinical monitoring (HIC-M; n = 625) or automated monitoring (AUT-M; n = 624) treatment. Cows were fitted with a neck-attached rumination and physical activity monitoring tag, and individual daily milk yield data were collected from parlor milk meters. For cows in HIC-M, clinical examination was conducted daily until 10 d in milk (DIM) and then in response to milk yield reduction alerts or visual observation of clinical signs of HD over the course of 21 DIM. For cows in AUT-M, clinical examination until 21 DIM was because of health index (HI) score alerts and reduced milk yield alerts. The HI score alerts used were generated based on the manufacturer's settings for the system for the last 2-h period before cows were selected for examination. Visual observation of clinical signs of HD was used for identifying cows potentially missed by automated alerts. Binomial and quantitative data were analyzed by logistic regression and ANOVA with repeated measures, respectively. The percentage of cows diagnosed with at least 1 HD during the experimental treatments risk period tended to be greater and the incidence rate ratio of HD diagnosed was greater in the HIC-M than in the AUT-M treatment. We found no difference between treatments for cows that exited the herd up to 60 or 150 DIM, but more cows tended to exit the herd from 61 to 150 DIM in the HIC-M than in the AUT-M treatment. No differences were detectable between treatments in daily or total milk yield to 21 DIM or in weekly mean milk yield and total milk yield to 150 DIM. More cows were inseminated in estrus for first service if in the HIC-M treatment and had no HD diagnosed than if in the HIC-M treatment but with HD diagnosed, or in the AUT-M treatment and had no HD diagnosed. Cows in the AUT-M treatment with HD diagnosed did not differ from other groups. No differences between treatments were observed in pregnancies per artificial insemination or pregnancy loss for first service. Despite a reduction in the risk of diagnosis of HD, no evidence indicated that a health monitoring program that relied on AHM system alerts to select cows for clinical examination reduced herd performance compared with a more intensive program that included systematic clinical examinations of all cows for the first 10 DIM, reduced milk yield alerts, and visual observation. However, to obtain the same herd performance as with the HIC-M treatment, the AUT-M treatment required use of visual observation. In conclusion, a health monitoring program designed to rely primarily on targeted clinical examination based on alerts from automated health monitoring systems might be a feasible alternative to programs that rely more on clinical examination, provided that visual observation is used to identify cows not detected by automated alerts.

Measuring dairy cow welfare with real-time sensor-based data and farm records: a concept study

Welfare assessment of dairy cows by in-person farm visits provides only a snapshot of welfare and is time-consuming and costly. Possible solutions to reduce the need for in-person assessments would be to exploit sensor data and other routinely collected on-farm records. The aim of this study was to develop an algorithm to classify dairy cow welfare based on sensors (accelerometer and/or milk meter) and farm records (e.g. days in milk, lactation number). In total, 318 cows from six commercial farms located in Finland, Italy and Spain (two farms each) were enrolled for a pilot study lasting 135 days. During this time, cows were routinely scored using 14 animal-based measures of good feeding, health and housing based on the Welfare Quality® (WQ®) protocol. WQ® measures were evaluated daily or approximately every 45 days, using disease treatments from farm records and on-farm visits, respectively. WQ® measures were supplemented with daily temperature-humidity index to account for heat stress. The severity and duration of each welfare measure were evaluated, and the final welfare index was obtained by summing up the values for each cow on each pilot study day, and stratifying the result into three classes: good, moderate and poor welfare. For model building, a machine-learning (ML) algorithm based on gradient-boosted trees (XGBoost) was applied. Two model versions were tested: (1) a global model tested on unseen herd, and (2) a herd-specific model tested on unseen part of the data from the same herd. The version (1) served as an example on the model performance on a herd not previsited by the evaluator, while version (2) resembled a custom-made solution requiring in-person welfare evaluation for model training. Our results indicated that the global model had a low performance with average sensitivity and specificity of 0.44 and 0.68, respectively. For the herd-specific version, the model performance was higher reaching an average of 0.64 sensitivity and 0.80 specificity. The highest classification performance was obtained for cows in poor welfare, followed by cows in good and moderate welfare (balanced accuracy of 0.77, 0.71 and 0.68, respectively). Since the global model had low classification accuracy, the use of the developed model as a stand-alone system based solely on sensor data is infeasible, and a combination of in-person and sensor-based welfare evaluation would be preferable for a reliable welfare assessment. ML-based solutions, even with fair discriminative abilities, have the potential to enhance dairy welfare monitoring.

Metabolic-digestive clinical disorders of lactating dairy cows were associated with alterations of rumination, physical activity, and lying behavior monitored by an ear-attached sensor

The objective of this observational cohort study was to characterize the pattern of rumination time (RT), physical activity (PA), and lying time (LT) monitored by an automated health monitoring system, based on an ear-attached sensor, immediately before, during, and after clinical diagnosis (CD) of metabolic-digestive disorders. Sensor data were collected from 820 lactating Holstein cows monitored daily from calving up to 21 DIM for detection of health disorders (HD). Cows were grouped retrospectively in the no-clinical health disorder group (NCHD; n = 616) if no HD were diagnosed, or the metabolic-digestive group (METB-DIG; n = 58) if diagnosed with clinical ketosis or indigestion only. Cows with another clinical health disorder within -7 to +7 d of CD of displaced abomasum, clinical ketosis, or indigestion were included in the metabolic-digestive plus one group (METB-DIG+1; n = 25). Daily RT, PA, and LT, and absolute and relative changes within -7 to +7 d of CD were analyzed with linear mixed models with or without repeated measures. Rumination time and PA were smaller, and LT was greater for the METB-DIG and METB-DIG+1 group than for cows in the NCHD group for most days from -7 to +7 d of CD of HD. In general, daily RT, PA, and LT differences were larger between the METB-DIG+1 and NCHD groups than between the METB-DIG and NCHD groups. In most cases, RT and PA decreased to a nadir and LT increased to a peak immediately before or after CD of HD, with a return to levels similar to the NCHD group within 7 d of CD. Absolute values and relative changes from 5 d before CD to the day of the nadir for RT and PA or peak for LT were different for cows in the METB-DIG and METB-DIG+1 group than for the NCHD group. For PA, the METB-DIG+1 group had greater changes than the METB-DIG group. For cows affected by metabolic-digestive disorders, RT, PA, and LT on the day of CD and resolution of clinical signs were different than for cows in the NCHD group, but an increase in RT and PA or a decrease in LT was observed from the day of CD to the day of resolution of clinical signs. We conclude that dairy cows diagnosed with metabolic-digestive disorders including displaced abomasum, clinical ketosis, and indigestion presented substantial alterations in the pattern of RT, PA, and LT captured by an ear-attached sensor. Thus, automated health monitoring systems based on ear-attached sensors might be used as an aid for identifying cows with metabolic-digestive disorders. Moreover, RT, PA, and LT changes after CD might be positive indicators of recovery from metabolic-digestive disorders.

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

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

Metritis and clinical mastitis events in lactating dairy cows were associated with altered patterns of rumination, physical activity, and lying behavior monitored by an ear-attached sensor

Understanding changes in parameters recorded by automated health monitoring systems based on ear-attached sensors on the days immediately before and after diagnosis of metritis and clinical mastitis can help develop dairy cow health monitoring strategies. The objective of this observational cohort study was to characterize rumination time, physical activity, and lying time monitored by an ear-attached sensor before, during, and after clinical diagnosis (CD) of metritis and clinical mastitis. Lactating Holsteins monitored daily for 21 d in milk for detection of health disorders were retrospectively included in the no clinical health disorder group (NCHD; n = 616) if no disorders were diagnosed. Cows were included in the metritis (MET; n = 69) or clinical mastitis (MAST; n = 36) group if diagnosed only with nonsevere metritis (watery, reddish, and fetid uterine discharge with or without pyrexia) or nonsevere clinical mastitis (visibly abnormal milk secretion with or without signs of udder inflammation, with no pyrexia and no systemic signs of disease), respectively. Cows diagnosed with severe metritis (signs of metritis plus systemic signs of disease) or severe clinical mastitis (signs of mastitis plus pyrexia and systemic signs of disease), and cows diagnosed with nonsevere metritis or clinical mastitis plus another disorder within -7 to +7 d of CD of metritis or clinical mastitis diagnosis, were included in the metritis plus (MET+; n = 25) or the clinical mastitis plus (MAST+; n = 15) group, respectively. Cows were fitted with an ear-attached accelerometer to measure rumination time, physical activity, and lying time. Mean daily values, mean value absolute change, and relative change for the mean daily value from 3 or 5 d before CD to the nadir for cows with metritis and clinical mastitis, respectively, were analyzed with linear mixed models with or without repeated measures. Rumination time and physical activity were lesser, and lying time was greater for the MET and MET+ groups than for the NCHD group for most days from -4 to +7 d of CD of metritis. Generally, daily rumination time, physical activity, and lying time differences were greater and more prolonged between the MET+ and NCHD than between the MET and NCHD groups. Similarly, cows in the MAST and MAST+ groups had lesser rumination time and physical activity than cows in the NCHD group for several days before diagnosis. Lying time was greater for the MAST+ than the NCHD group on d -1 and 0 relative to CD. Absolute values and relative changes from 3 d before CD to the day of the nadir for rumination time and physical activity, or peak for lying time, were different for cows in the MET and MET+ groups than for the NCHD group. Similar results were observed for the MAST and MAST+ groups compared with the NCHD group. For cows with metritis, either an increase in rumination time and physical activity or a decrease in lying time was observed from the day of CD to resolution of clinical signs, but no changes were observed for the NCHD. Cows with clinical mastitis and the NCHD group had different rumination times, physical activity, and lying times on the day of CD and resolution of clinical signs, but cows with clinical mastitis had no significant changes from the day of CD to resolution of clinical signs. We conclude that cows affected by metritis and clinical mastitis presented substantial alterations of the patterns of rumination time, physical activity, and lying time captured by an ear-attached sensor. Thus, automated health monitoring systems based on ear-attached sensors might be used as an aid for identifying cows with metritis and clinical mastitis. Moreover, behavioral parameter changes after CD might be good indicators of resolution of clinical signs of metritis but not mastitis.

Comparative Analysis of Rumen Microbiota Composition in Dairy Cows with Simple Indigestion and Healthy Cows

Simple indigestion in cows leads to substantial economic losses in the dairy industry. Despite ongoing efforts, an effective treatment for this issue remains elusive. Previous studies have emphasized the vital role of rumen microbes in maintaining ruminant health. To deepen our comprehension of the intricate interplay between rumen microbiota and simple indigestion, we undertook a study involving the analysis of rumen fluid from eight cows with simple indigestion and ten healthy cows. Additionally, we collected data pertaining to milk production, rumination behavior, and rumen characteristics. The results showed that cows with simple indigestion displayed significantly lower milk yield, reduced rumination duration, and weakened rumen contraction when contrasted with the healthy cows (p < 0.05). However, no significant difference in microbiota α-diversity emerged (p > 0.05). Principal coordinate analysis (PCoA) illuminated substantial variations in rumen microbial structure among the two groups (p < 0.05). Further analysis spotlighted distinctive bacteria in the rumen of the cows with indigestion, including Allisonella, Synergistes, Megasphaera, Clostridium_XIVb, Campylobacter, and Acidaminococcus. In contrast, Coraliomargarita, Syntrophococcus, and Coprococcus are the dominant bacterial genera in the rumen of healthy dairy cows. Importantly, these key bacterial genera also dominated the overarching microbial interaction network. The observation suggests that changes in the abundance of these dominant bacterial genera potentially underlie the principal etiology of cows with simple indigestion. The present findings can provide insights into simple indigestion prevention and treatment in dairy cows.

Association between Prepartum Alerts Generated Using a Commercial Monitoring System and Health and Production Outcomes in Multiparous Dairy Cows in Five UK Herds

Identifying cows that are at greater risk for disease prior to calving would be a valuable addition to transition management. Prior to the commercial release of software features in an automated behavioral monitoring system, designed to identify cows in the dry period at greater risk of disease postpartum, a retrospective analysis was carried out in five dairy herds to evaluate whether the software could identify prepartum cows that subsequently received health treatments postpartum and whether prepartum alerts (transition alerts) are associated with a reduction in milk production in the subsequent lactation. Herd management and production records were analyzed for cows receiving treatment in the first 21 d of lactation (days in milk, DIM) for clinical mastitis, reproductive tract disease (metritis, retained fetal membranes), metabolic disease (hypocalcemia, ketosis and displaced abomasum) and for cows exiting the herd by 60 DIM. Data was gathered for 986 cows, 382 (38.7%) of which received a transition alert and 604 (61.3%) that did not. During the first 21 DIM 312 (31.6%) cows went on to receive a disease treatment, of these 51.9% (n = 162/312) were transition alert cows and 48.1% (n = 150/312) non-transition alert cows, while 8.6% (n = 33/382) alert cows exited the herd by 60 DIM compared to 4.8% (n = 29/604) of cows that did not receive an alert. A cow receiving a transition alert (OR = 1.76, 95% confidence interval (CI) = 1.27-2.44) and increasing parity (OR = 2.03, 95% CI = 1.44-2.86) were both associated with increased risk of receiving a disease treatment in the first 21 DIM. The occurrence of a transition alert was negatively associated with both week 4 milk yield (daily average yield in fourth week of lactation) and predicted 305 d yield. Transition alerts correctly predicted 62.5% (95% CI: 59.3-65.5) of treatments with a sensitivity of 42.4% (95% CI: 37.4-45.5) and a specificity of 75.2% (95% CI: 71.5-78.6). Associations were identified between postpartum health and production outcomes and prepartum behavioral measures from an automated activity monitoring system.

Classifying Chewing and Rumination in Dairy Cows Using Sound Signals and Machine Learning

This research paper introduces a novel methodology for classifying jaw movements in dairy cattle into four distinct categories: bites, exclusive chews, chew-bite combinations, and exclusive sorting, under conditions of tall and short particle sizes in wheat straw and Alfalfa hay feeding. Sound signals were recorded and transformed into images using a short-time Fourier transform. A total of 31 texture features were extracted using the gray level co-occurrence matrix, spatial gray level dependence method, gray level run length method, and gray level difference method. Genetic Algorithm (GA) was applied to the data to select the most important features. Six distinct classifiers were employed to classify the jaw movements. The total precision found was 91.62%, 94.48%, 95.9%, 92.8%, 94.18%, and 89.62% for Naive Bayes, k-nearest neighbor, support vector machine, decision tree, multi-layer perceptron, and k-means clustering, respectively. The results of this study provide valuable insights into the nutritional behavior and dietary patterns of dairy cattle. The understanding of how cows consume different types of feed and the identification of any potential health issues or deficiencies in their diets are enhanced by the accurate classification of jaw movements. This information can be used to improve feeding practices, reduce waste, and ensure the well-being and productivity of the cows. The methodology introduced in this study can serve as a valuable tool for livestock managers to evaluate the nutrition of their dairy cattle and make informed decisions about their feeding practices.

Behavioral patterns as indicators of resilience after parturition in dairy cows

During the transition phase, dairy cows are susceptible to develop postpartum diseases. Cows that stay healthy or recover rapidly can be considered to be more resilient in comparison to those that develop postpartum diseases. An indication of loss of resilience will allow for early intervention with preventive and supportive measures before the onset of disease. We investigated which quantitative behavioral characteristics during the dry period could be used as indicators of reduced resilience after calving, using noninvasive Smart Tag neck and Smart Tag leg sensors in dairy cows (Nedap N.V.). We followed 180 cows during 2 wk before until 6 wk after parturition at 4 farms in the Netherlands. Serving as proxy for loss of resilience, as defined by the duration and severity of disease, a clinical assessment was performed twice weekly and blood samples were taken in the first and fifth week after parturition. For each cow, clinical and serum value deviations were aggregated into a total deficit score (TDS total). We also calculated TDS values relating to inflammation, locomotion, or metabolic problems, which were further divided into macro-mineral and liver-related deviations. Smart Tag neck and leg sensors provided continuous behavioral activity signals of which we calculated the average, variance, and autocorrelation during the dry period. Diurnal patterns in the behavioral activity signals were derived by fast Fourier transformation and the calculation of the nonperiodicity. To select significant predictors of resilience, we first performed a univariate analysis with TDS as dependent variable and the behavioral characteristics that were measured during the dry period, as potential predictors with cow as experimental unit. We included parity group as fixed effect and farm as random effect. Next, we performed multivariable analysis with only significant predictors, followed by a variable selection procedure to obtain a final linear mixed model with an optimal subset of predictors with parity group as fixed effect and farm as random effect. The TDS total was best predicted by average inactive time, nonperiodicity ruminating, nonperiodicity of bouts standing up and fast Fourier transformation stand still. Average inactive time was negatively correlated with average eating time, and these 2 predictors could be exchanged with only little difference in model performance. Our best performing model predicted TDS total at a cutoff level of 60 points, with a sensitivity of 79.5% and a specificity of 73.2% with a positive predicted value of 0.69 and a negative predicted value of 0.83. The models to predict the other TDS categories showed a lower predictive performance as compared with the TDS total model, which could be related to the limited sample size and therefore, low occurrence of problems within a specific TDS category. Furthermore, more resilient dairy cows are characterized by high averages of eating time with high regularity in rumination and low averages of inactive time. They reveal high regularity in standing time and transitions from lying to standing, in the dry period. These behaviors can be used as indicators of resilience and allow for preventive intervention during the dry period in vulnerable dairy cattle. However, further examination is still required to find clues for adequate intervention strategies.

Effectiveness of the strain 919 bovine ephemeral fever virus vaccine in the face of a real-world outbreak: A field study in Israeli dairy herds

Bovine ephemeral fever virus (BEFV) is a globally spread arthropod-borne RNA virus that has significant economic impacts on the cattle industry. A live attenuated commercial BEF vaccine, based on the Australian BEFV strain 919, is widely used in Israel and other countries. A previous study has suggested the high effectiveness of this vaccine (ULTRAVAC BEF VACCINE™ from Zoetis®), but anecdotal reports of high BEF morbidity among vaccinated dairy herds in Israel casted doubt on these findings. To resolve this uncertainty, a randomized controlled field vaccine effectiveness study was conducted in Israel during a BEF outbreak which occurred in 2021. Eleven dairy herds were enrolled and monitored for BEF-associated morbidity and rumination alteration patterns using electronic monitoring tags (HR Tags, SCR® Dairy, Netanya, Israel). Four of the herds were naturally infected with BEFV during the outbreak, resulting in a total of 120 vaccinated and 311 unvaccinated subjects that were included in the effectiveness study. A mixed-effect Cox proportional hazard regression model was used to calculate the overall hazard ratio between vaccinated and unvaccinated cattle. This analysis demonstrated an average vaccine effectiveness of 60 % (95 % CI = 38 %-77 %) for preventing clinical disease. In addition, a non-statistically significant trend (p = 0.1) towards protection from mortality was observed, with no observation of mortality among the vaccinated groups compared to 2.61 % mortality (7/311) among the unvaccinated subjects. One hundred and thirty vaccinated and unvaccinated calves from affected and non-affected herds and with different status of morbidity were sampled and analysed by serum-neutralization test. The highest titers of BEFV-neutralizing antibodies were found in subjects that were both vaccinated and clinically affected, indicating a booster effect after vaccination. The results of the study provide evidence for the moderate effectiveness of the ULTRAVAC BEF VACCINE™ for the prevention of BEF.

A randomized clinical trial evaluating the effects of administration of acidogenic boluses at dry-off on rumination and activity behavior in the 14 subsequent days

Elevated milk production at dry-off can lead to increased udder pressure and, in turn, increased stress due to pain and discomfort, affecting natural behaviors. Administering acidogenic boluses at dry-off acts by inducing temporary and mild decreases in blood pH. This decreases dry matter intake, reduces milk yield, and increases cow comfort by lessening udder pressure. The objective of this study was to assess the effect of oral administration of acidogenic boluses at dry-off on total daily activity (TDA) and total daily rumination (TDR) behaviors in the first 2 wk of the dry period. This randomized clinical trial was conducted on a single farm and cows were randomly assigned to either treatment (TRT; n = 30) or control (CON; n = 34). The TRT group received 2 acidogenic boluses at dry-off and the CON group received no intervention. All cows received dry-cow therapy (intramammary antibiotic and internal teat sealant). The TDA and TDR data from 7 d before to 14 d after dry-off were measured using ear-mounted activity monitors. Analyses were performed using linear mixed-effects models with repeated measures. We observed a similar TDA in both groups throughout the study follow-up period. Overall, cows in the TRT group spent 17 min/d less time active than cows in the CON group in the first 2 wk after dry-off with the greatest difference observed on the second day of the dry period (TRT = 395 min/d; 95% CI: 370 to 420 vs. CON = 428 min/d; 95% CI: 404 to 451). The TRT group had lower TDR in the first 24 h after bolus administration (TRT = 437 min/d; 95% CI: 414 to 461 vs. CON = 488 min/d; 95% CI: 466 to 510) when compared with the CON group, but no differences were observed when comparing both groups in the 13 subsequent days. Our results indicate that administering acidogenic boluses at dry-off slightly decreased TDA during the first 2 wk of the dry period and decreased TDR on the first day after administration.

The Association between Prepartum Rumination Time, Activity and Dry Matter Intake and Subclinical Hypocalcemia and Hypomagnesemia in the First 3 Days Postpartum in Holstein Dairy Cows

Changes in prepartum behaviors such as total daily rumination (TDR), total daily activity (TDA) and dry matter intake (DMI) have the potential to be used as early indicators for cows at risk for subclinical hypocalcemia (SCH) or hypomagnesemia (HYM) after calving. Our objective was to investigate associations between average daily rate of change in total daily rumination (ΔTDR), total daily activity (ΔTDA) and dry matter intake (ΔDMI) from -3 days prepartum to calving with SCH and HYM at D0 or D3 relative to calving. Prepartum TDR, TDA and DMI were measured in 64 Holstein dairy cows. Blood samples were taken at D0 and D3 post-calving for the measurement of total plasma Ca and Mg concentration. Linear regression models were used to analyze the association between ΔTDR, ΔTDA and ΔDMI and SCH and HYM at D0 and D3 relative to calving. Potential confounding variables were offered to the models and backwards selection was used to determine which covariates to retain. No significant differences in prepartum ΔTDR, ΔTDA or ΔDMI were found between cows with or without SCH and HYM at D0 and D3. Our results suggest that the change in TDR, TDA and DMI in the last 3 days prepartum are not effective predictors for cows that will have SCH or HYM in the first 3 days postpartum.

Automated monitoring and detection of disease using a generic facial feature scoring system - A case study on FMD infected cows

Digital images are becoming more readily available and possibilities for image processing are developing rapidly. This opens the possibility to use digital images to monitor and detect diseases in animals. In this paper we present 1) a generic facial feature scoring system based on seven facial features, 2) manual scores of images of Holstein Frisian heifers during foot-and-mouth disease vaccine efficacy trials and 3) automatic disease scores of the same animals. The automatic scoring system was based on the manual version and trained on annotated images from the manual scoring system. For both systems we found an increase in disease scores three days post infection, followed by a recovery. This temporal pattern matched with observations made by animal caretakers. Importantly, the automatic system was able to discern animals that were protected by the vaccine, and did not develop blisters at the feet, and animals that were not protected. Finally, automatic scores could be used to detect healthy and sick animals with a sensitivity and specificity of 0.94 on the second and third days following infection in an experimental setting. This generic facial feature disease scoring system could be further developed and extended to lactating Holstein Frisian dairy cows, other breeds and other infectious diseases. The system could be applied during animal experiments or, after further development, in a farm setting.

[Digitalisation in cattle practice - results of an online-survey in Austria]

OBJECTIVES

The use of digital technologies is increasing in modern livestock farming and veterinary practice. The aim of this online survey among Austrian cattle practitioners was to increase knowledge concerning the acceptance and use of digital (sensor) technologies.

MATERIAL AND METHODS

The link to the survey was sent by the Austrian animal health services (TGD) via email to the registered veterinarians. A total of 115 veterinarians participated in the survey.

RESULTS

Most of the participants were convinced that digitalisation associated with improvements in their profession in terms of economy, time-savings, collaboration with colleagues and working efficiency. The agreement ranged between 60% and 79%. On the other hand, concerns regarding data security (41%) were also raised. When asked whether they would recommend sensor systems to farmers, approximately 45% of the participants answered yes, 36% declined, 19% were undecided. From a list of specified sensors and technologies, monitoring by cameras (68%), automatic concentrate feeding systems (63%) and activity sensors (61%) were considered as beneficial for animal health. Concerning an assessment of the animals' health status the majority of respondents (58%) would trust conventional methods more than sensor systems. Data provided by farmers is mainly used to improve the understanding of patients' disease progression (67%) as well as to comply with documentation requirements (28%). In addition, we asked whether the participants could imagine running a telemedicine practice. On a scale ranging from 1 to 100, initial agreement amounted to a median of 20 which then decreased to a median of 4 in the repeated question at the end of the questionnaire.

CONCLUSIONS

The veterinarians saw advantages in using digital technologies both in their daily work and to improve animal health management. In some areas, however, clear reservations were evident . A telemedical offer does not seem to be relevant for the majority of the participants in the context of the description provided.

CLINICAL RELEVANCE

The results are intended to help identify areas in which more information is needed for veterinarians and to capture a picture of opinions that could be relevant for the changing collaboration between farmers and veterinarians.

An automated system for cattle reproductive management under the IoT framework. Part I: the e-Synch system and cow responses

The objective of this manuscript was to present the e-Synch system, integrating an intravaginal electronically controlled hormone delivery and sensing device with an IoT platform for remote programming and monitoring. Secondary objectives were to demonstrate system functionality and cow responses to e-Synch. External components of e-Synch include a 3D-printed case with retention wings, a flexible wideband antenna, and silicone membrane for pressure balancing. Internal components include a central control board, battery, wireless charging coil, and two silicone hormone reservoirs connected to individual peristaltic pumps. An accelerometer and a high-accuracy temperature sensor are integrated in the custom printed circuit board (PCB). The IoT platform includes a gateway consisting of Raspberry PI 3 and a CC1352 radiofrequency module that collects sensor data at 915 mHz. Data is transferred to the Google Cloud utilizing the IoT Core service through TCP/IP, and then is pulled by the Pub/Sub service. After routing to a BigQuery table by the Dataflow service, data visualization is provided by Data Studio. Drug delivery protocols are selected using an IOS device app that connects to e-Synch through Bluetooth. Experiments with lactating Holsteins cows were conducted to demonstrate proof-of-concept system functionality and evaluate cow responses. Despite unstable communication and signal discontinuity because of signal strength attenuation by body tissue, devices (n=6) communicated with the IoT platform in 89% (24/27) of use instances. Temperature and accelerometer data were received for at least one 15 min period during an 8 h insertion period from all devices that communicated with the IoT platform. Variation in accelerometer data (± 8.565 m/s2) was consistent with cow activity during experimentation and mean vaginal temperature of 39.1 °C (range 38.6 to 39.5 °C) demonstrated sensor functionality. Hormone release was confirmed in all instances of device use except for one. Cow behavior evaluated through signs of discomfort and pain, and tail raising scores was mostly unaltered by e-Synch. Vaginal integrity and mucus scores also remained unaltered during and after device insertion. In conclusion, the e-Synch device integrated with a controlling app and IoT platform might be used to automate intravaginal hormone delivery and sensing for controlling the estrous cycle of cattle.

Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases

Precision livestock farming has a crucial function as farming grows in significance. It will help farmers make better decisions, alter their roles and perspectives as farmers and managers, and allow for the tracking and monitoring of product quality and animal welfare as mandated by the government and industry. Farmers can improve productivity, sustainability, and animal care by gaining a deeper understanding of their farm systems as a result of the increased use of data generated by smart farming equipment. Automation and robots in agriculture have the potential to play a significant role in helping society fulfill its future demands for food supply. These technologies have already enabled significant cost reductions in production, as well as reductions in the amount of intensive manual labor, improvements in product quality, and enhancements in environmental management. Wearable sensors can monitor eating, rumination, rumen pH, rumen temperature, body temperature, laying behavior, animal activity, and animal position or placement. Detachable or imprinted biosensors that are adaptable and enable remote data transfer might be highly important in this quickly growing industry. There are already multiple gadgets to evaluate illnesses such as ketosis or mastitis in cattle. The objective evaluation of sensor methods and systems employed on the farm is one of the difficulties presented by the implementation of modern technologies on dairy farms. The availability of sensors and high-precision technology for real-time monitoring of cattle raises the question of how to objectively evaluate the contribution of these technologies to the long-term viability of farms (productivity, health monitoring, welfare evaluation, and environmental effects). This review focuses on biosensing technologies that have the potential to change early illness diagnosis, management, and operations for livestock.

Association between Rumination Times Detected by an Ear Tag-Based Accelerometer System and Rumen Physiology in Dairy Cows

Monitoring rumination activity is considered a useful indicator for the early detection of diseases and metabolic disorders. Accelerometer-based sensor systems provide health alerts based on individual thresholds of rumination times in dairy cows. Detailed knowledge of the relationship between sensor-based rumination times and rumen physiology would help detect conspicuous animals and evaluate the treatment's success. This study aimed to investigate the association between sensor-based health alerts and rumen fluid characteristics in Holstein-Friesian cows at different stages of lactation. Rumen fluid was collected via a stomach tube from 63 pairs of cows with and without health alerts (ALRT vs NALRT). Pairs were matched based on the day of lactation, the number of lactations, and health criteria. Rumen fluid was collected during and after health alerts. The parameters of color, odor, consistency, pH, redox potential, sedimentation flotation time, and the number of protozoa were examined. Results showed differences between both groups in odor, rumen pH, sedimentation flotation time, and protozoan count at the first rumen fluid collection. Within the groups, greater variations in rumen fluid parameters were found for ALRT cows compared to NALRT cows. The interaction between health alert and stage of lactation did not affect the rumen fluid parameters.

Evaluation of an indwelling bolus equipped with a triaxial accelerometer for the characterisation of the diurnal pattern of bovine reticuloruminal contractions

This observational study aimed to describe the diurnal pattern of reticuloruminal contraction rate (RRCR) and the proportion of time spent ruminating by cattle, using two commercial devices equipped with triaxial accelerometers: an indwelling bolus (placed in the reticulum) and a neck collar. The three objectives of this study were firstly to determine whether the indwelling bolus provided observations consistent with RRCR as determined by clinical examination using auscultation and ultrasound, secondly to compare estimates of time spent ruminating using the indwelling bolus and a collar-based accelerometer, and finally to describe the diurnal pattern of RRCR using the indwelling bolus data. Six rumen-fistulated, non-lactating Jersey cows were fitted with an indwelling bolus (SmaXtec Animal Care GmbH, Graz, Austria) and a neck collar (Silent Herdsman, Afimilk Ltd. Kibbutz Afikim, Israel), and data were collected over two weeks. Cattle were housed together in a single straw-bedded pen and fed ad libitum hay. To assess the agreement between the indwelling bolus and traditional methods of assessing reticuloruminal contractility in the first week, the RRCR was determined over 10 min, twice a day, by ultrasound and auscultation. Mean inter-contraction intervals (ICI) derived from bolus and ultrasound, and from auscultation were 40.4 ± 4.7, 40.1 ± 4.0 and 38.4 ± 3.3 s. Bland-Altmann plots showed similar performance of the methods with small biases. The Pearson correlation coefficient for the time spent ruminating derived from neck collars and indwelling boluses was 0.72 (highly significant, P < 0.001). The indwelling boluses generated a consistent diurnal pattern for all the cows. In conclusion, a robust relationship was observed between clinical observation and the indwelling boluses for estimation of ICI and, similarly, between the indwelling bolus and neck collar for estimating rumination time. The indwelling boluses showed a clear diurnal pattern for RRCR and time spent ruminating, indicating that they should be useful for assessing reticuloruminal motility.

Relationship between reticular pH parameters and potential on-farm indicators in the early lactation of dairy cows

Subacute ruminal acidosis (SARA) is an important nutritional disorder affecting animal welfare and economy of milk production. Definitions rely on ruminal pH but due to limitations of its measurement, indicators reflecting low pH are highly desirable. The aim of this study was to investigate the relationship between reticular pH and 18 on-farm indicators in milk, blood, faeces, urine and chewing behaviour in early lactating dairy cows. Ten farms were visited for 3 weeks and in total samples of 100 cows (10 per farm) were taken. The statistics and graphical visualization were performed using Pearson correlation and linear regression models on an animal individual level as well as with linear mixed models. Eight indicators (milk fat, fat-to-protein ratio, rumination time, feed intake time, rumination frequency, rumination boluses, lying time and faecal pH) were statistically significant associated with the daily animal individual reticular pH average. However, none of the models including the potential explanatory variables explained more than 5% of the pH variations. The study confirms the necessity of pH measurement to detect SARA risk animals in early lactation dairy cows.

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.

Integrated infrared thermography and accelerometer-based behavior logger as a hoof lesion identification tool in dairy cows with various foot diseases under subtropical climates

Infrared thermography (IRT) can measure a temperature change on the surface of objects, and is widely used as an inflammation or fever detection tool. The objective of this longitudinal study was to investigate the feasibility of detecting hoof lesion cattle using IRT under subtropical climate conditions. The experiment was conducted in two free-stall commercial dairy farms and 502 dairy cows participated between August 2020 and March 2022. Before hoof trimming, the portable IRT was used to measure the maximum temperature of each hoof from three shooting directions, including anterior (hoof coronary band), lateral (hoof lateral coronary band), and posterior (skin between heel and bulbs). In order to evaluate the effect of hoof lesions on the behavior of dairy cows, we also collected behavior data by automated accelerometers. The results indicated that the temperature of hooves with lesions was significantly higher than that of sound hooves in hot environments regardless of the shooting directions (P < 0.0001). In all of three shooting directions, the maximum temperature of feet with severe lesion was significantly higher than those of feet with mild lesion and sound feet (P < 0.05). Cows with lesion feet had lower daily activity and feeding time than sound cows before clinical diagnosis (P < 0.05). Furthermore, we used thresholds of both anterior hoof temperature at 32.05 °C and average daily activity at 410.5 (arbitrary unit/d) as a lame cow detecting tool. The agreement of this integrated tool reached 75% with clinical diagnosis, indicating that this integrated approach may be feasible for practice in dairy farm. In conclusion, IRT has the potential to be used as a hoof lesion detecting tool under subtropical climate conditions when using sound hoof temperature as reference points, and detection precision can be improved when IRT integrated with automated accelerometers as a lame cow detecting tool.

Evaluation of Heat Stress Effects in Different Geographical Areas on Milk and Rumen Characteristics in Holstein Dairy Cows Using Robot Milking and Rumen Sensors: A Survey in South Korea

This survey investigated, using robotic milking and rumen sensors, the effects of an adjusted temperature−humidity index (THI) in different geographical areas on milk yield, fat and protein, rumen temperature, and activity in lactating Holstein cows. We additionally explored the effect of parity on milk and rumen temperature and activity under different THI levels during the summer. From January to September 2020, four farms (276 dairy cows) were subjected to the use of robot milking machines, and two farms (162 dairy cows) to the use of rumen sensors. For the temperature and humidity data, the THI was calculated on the basis of the data from the Korea Meteorological Administration (KMA). The data were analyzed using the GLM procedure of SAS. Milk yield and milk protein decreased (p < 0.05), and milk fat increased (p < 0.05) at all farms during the summer, from July to August, when the temperature and humidity were high (THI = 72−79). Milk yields were the highest in the fifth, sixth, seventh, and eighth parities, and the lowest in the fourth (p < 0.05). Milk fat concentration was the highest in the fourth parity and the lowest in the first parity (p < 0.05). In the first parity, the highest levels of milk protein and lactose were seen (5.24% and 4.90%, respectively). However, milk protein concentration was the lowest in the third parity, and the lactose concentration was the lowest in the fifth, sixth, seventh, and eighth parities. According to the rumen sensor, the rumen temperature of the dairy cows at the two farms also continued to increase (p < 0.05) from July to August, and then decreased (p < 0.05) in September. However, the activity in the rumen was increased (p < 0.05) from July to September. In the second parity, the highest rumen temperature (39.02 °C) was observed, while the lowest value (38.28 °C) was observed in the third parity. The highest value of rumen activity (12.26 mg) was observed in the second parity and the lowest value (11.31 mg) in the fourth parity. These data, taken together, confirm that a high THI during summer conditions negatively affects milk yield, milk protein content, and rumen temperature and activity in lactating Holstein cows. It is also demonstrated that various parities affect milk characteristics and the rumen environment in the summer season.

High Precision Classification of Resting and Eating Behaviors of Cattle by Using a Collar-Fitted Triaxial Accelerometer Sensor

Cattle are less active than humans. Hence, it was hypothesized in this study that transmitting acceleration signals at a 1 min sampling interval to reduce storage load has the potential to improve the performance of motion sensors without affecting the precision of behavior classification. The behavior classification performance in terms of precision, sensitivity, and the F1-score of the 1 min serial datasets segmented in 3, 4, and 5 min window sizes based on nine algorithms were determined. The collar-fitted triaxial accelerometer sensor was attached on the right side of the neck of the two fattening Korean steers (age: 20 months) and the steers were observed for 6 h on day one, 10 h on day two, and 7 h on day three. The acceleration signals and visual observations were time synchronized and analyzed based on the objectives. The resting behavior was most correctly classified using the combination of a 4 min window size and the long short-term memory (LSTM) algorithm which resulted in 89% high precision, 81% high sensitivity, and 85% high F1-score. High classification performance (79% precision, 88% sensitivity, and 83% F1-score) was also obtained in classifying the eating behavior using the same classification method (4 min window size and an LSTM algorithm). The most poorly classified behavior was the active behavior. This study showed that the collar-fitted triaxial sensor measuring 1 min serial signals could be used as a tool for detecting the resting and eating behaviors of cattle in high precision by segmenting the acceleration signals in a 4 min window size and by using the LSTM classification algorithm.

The Early Prediction of Common Disorders in Dairy Cows Monitored by Automatic Systems with Machine Learning Algorithms

We use multidimensional data from automated monitoring systems and milking systems to predict disorders of dairy cows by employing eight machine learning algorithms. The data included the season, days in milking, parity, age at the time of disorders, milk yield (kg/day), activity (unitless), six variables related to rumination time, and two variables related to the electrical conductivity of milk. We analyze 131 sick cows and 149 healthy cows with identical lactation days and parity; all data are collected on the same day, which corresponds to the diagnosis day for disordered cows. For disordered cows, each variable, except the ratio of rumination time from daytime to nighttime, displays a decreasing/increasing trend from d-7 or d-3 to d0 and/or d-1, with the d0, d-1, or d-2 values reaching the minimum or maximum. The test data sensitivity for three algorithms exceeded 80%, and the accuracies of the eight algorithms ranged from 65.08% to 84.21%. The area under the curve (AUC) of the three algorithms was >80%. Overall, Rpart best predicts the disorders with an accuracy, precision, and AUC of 81.58%, 92.86%, and 0.908, respectively. The machine learning algorithms may be an appropriate and powerful decision support and monitoring tool to detect herds with common health disorders.

Sensor based time budgets in commercial Dutch dairy herds vary over lactation cycles and within 24 hours

Cows from 8 commercial Dutch dairy farms were equipped with 2 sensors to study their complete time budgets of eating, rumination, lying, standing and walking times as derived from a neck and a leg sensor. Daily sensor data of 1074 cows with 3201 lactations was used from 1 month prepartum until 10 months postpartum. Farms provided data over a 5 year period. The final models (lactational time budget and 24h time budget) showed significant effects of parity, farm and calving season. When primiparous cows were introduced in the lactational herd, they showed a decrease in lying time of 215 min (95% CI: 187–242) and an increase in standing time of 159 min (95% CI: 138–179), walking time of 23 min (95% CI: 20–26) and rumination time of 69 min (95% CI: 57–82). Eating time in primiparous cows increased from 1 month prepartum until 9 months in lactation with 88 min (95% CI: 76–101) and then remained stable until the end of lactation. Parity 2 and parity 3+ cows decreased in eating time by 30 min (95% CI: 20–40) and 26 min (95% CI: 18–33), respectively, from 1 month before to 1 month after calving. Until month 6, eating time increased 11 min (95% CI: 1–22) for parity 2, and 24 min (95% CI: 16–32) for parity 3+. From 1 month before calving to 1 month after calving, they showed an increase in ruminating of 17 min (95% CI: 6–28) and 28 min (95% CI: 21–35), an increase in standing time of 117 min (95% CI: 100–135) and 133 min (95% CI: 121–146), while lying time decreased with 113 min (95% CI: 91–136) and 130 min (95% CI: 114–146), for parity 2 and 3+, respectively. After month 1 in milk to the end of lactation, lying time increased 67 min (95% CI: 49–85) for parity 2, and 77 min (95% CI: 53–100) for parity 3+. Lactational time budget patterns are comparable between all 8 farms, but cows on conventional milking system (CMS) farms with pasture access appear to show higher standing and walking time, and spent less time lying compared to cows on automatic milking system (AMS) farms without pasture access. Every behavioral parameter presented a 24h pattern. Cows eat, stand and walk during the day and lie down and ruminate during the night. Daily patterns in time budgets on all farms are comparable except for walking time. During the day, cows on CMS farms with pasture access spent more time walking than cows on AMS farms without pasture access. The average 24h pattern between parities is comparable, but primiparous cows spent more time walking during daytime compared to older cows. These results indicate a specific behavioral pattern per parameter from the last month prepartum until 10 months postpartum with different patterns between parities but comparable patterns across farms. Furthermore, cows appear to have a circadian rhythm with varying time budgets in the transition period and during lactation.

Alteration in Activity Patterns of Cows as a Result of Pain Due to Health Conditions

Simple Summary

There are several conditions and diseases considered painful to cattle. One reason for the inconsistency in pain recognition and thus pain relief in cattle is the inadequate ability to identify and assess pain. In fact, both increased and/or reduced daily lying time can be indicative of pain in cattle. This review helps to properly interpret pain in cows through behavioural activity patterns and explores whether pain relief is capable to restore their normal activity.

Abstract

The main conditions and diseases considered painful in dairy cows are mastitis, lameness, calving (including dystocia and caesarean section) and metritis. The cattle literature reports that deviation from normal daily activity patterns (both increased and/or reduced daily lying time) can be indicative of painful conditions and diseases in cows. This narrative review discusses on how pain due to several health conditions in dairy cows modifies its activity pattern and explores if non-steroidal anti-inflammatory drugs (NSAIDs) are capable of restoring it. Divergent outcomes may differ depending upon the painful cause, the severity and the moment, and consequently its interpretation should be properly explained. For instance, cows with clinical mastitis reduced their time lying and increased the number of lying bouts and stepping due to pain caused by the swollen udder when cows are lying. However, lame cows show longer lying times, with a lower number of lying bouts and longer and more variable lying bouts duration, as compared to non-lame cows. When the relationship between painful disorders and daily activity patterns is studied, factors such as parity, bedding type and severity of disease are important factors to take into consideration. The potential benefits of the NSAIDs treatment in painful health disorders depend upon the type of drug administered, its dosage and administration mode, and the time of administration relative to the painful health disorder. This narrative review can be used as a tool to properly interpret and grade pain in cows through behavioural activity patterns and proposes directions for future investigations.

Peripartal Rumen-Protected L-Carnitine Manipulates the Productive and Blood Metabolic Responses in High-Producing Holstein Dairy Cows

This study aimed to monitor the effect of including rumen-protected L-carnitine (Carneon 20 Rumin-Pro, Kaesler Nutrition GmbH, Cuxhaven, Germany) in the transition diet on the productive and metabolic responses of multiparous high-producing Holstein dairy cows. Thirty-two multiparous cows were allocated in a completely randomized design to receive the same diet plus 60 g fat prill containing 85% palmitic acid (control, n = 16) or 100 g rumen-protected L-carnitine (RLC, n = 16); at 28 days before expected calving until 28 days in milk (DIM). Fat prill was included in the control diet to balance the palmitic acid content of both experimental diets. Milk production over the 28 DIM for the control and RLC groups was 46.5 and 47.7 kg, respectively. Milk fat content tended to increase upon rumen-protected L-carnitine inclusion (p = 0.1). Cows fed rumen-protected L-carnitine had higher fat- and energy-corrected milk compared with the control group. Pre- and post-partum administration of L-carnitine decreased both high- and low-density lipoprotein concentrations in peripheral blood of post-partum cows. The results of this study indicated that the concentration of triglycerides and beta-hydroxybutyrate was not significantly different between the groups, whereas the blood non-esterified fatty acid concentration was markedly decreased in cows supplemented with L-carnitine. Animals in the RLC group had a significant (p < 0.05) lower blood haptoglobin concentration at 7 and 14 DIM than the control. Animals in the RLC group had a lower concentration of blood enzymes than those of the control group. The mRNA abundance of Toll-like receptors 4, cluster of differentiation 14, and myeloid differential protein 2 did not significantly change upon the supplementation of L-carnitine in the transition diet. In summary, the dietary inclusion of RLC improved dairy cow's performance during the early lactation period. Greater production, at least in part, is driven by improved energy utilization efficiency and enhanced metabolic status in animals during the periparturient period.

Time-Consuming, but Necessary: A Wide Range of Measures Should Be Included in Welfare Assessments for Dairy Herds

Animal welfare assessments that measure welfare outcomes, including behavior and health, can be highly valid. However, the time and skill required are major barriers to their use. We explored whether feasibility of welfare outcome assessment for dairy herds may be improved by rationalizing the number of measures included. We compared two approaches: analyzing whether strong pairwise associations between measures existed, enabling the subsequent exclusion of associated measures; and identifying possible summary measures—“iceberg indicators”—of dairy herd welfare that could predict herd welfare status. A cross-sectional study of dairy herd welfare was undertaken by a single assessor on 51 English farms, in which 96 welfare outcome measures were assessed. All measures showed at least one pairwise association; percentage of lame cows showed the most (33 correlations). However, most correlations were weak–moderate, suggesting limited scope for excluding measures from protocols based on pairwise relationships. A composite measure of the largest portion of herd welfare status was then identified via Principal Component Analysis (Principal Component 1, accounting for 16.9% of variance), and linear regression revealed that 22 measures correlated with this. Of these 22, agreement statistics indicated that percentage of lame cows and qualitative descriptors of “calmness” and “happiness” best predicted Principal Component 1. However, even these correctly classified only ~50% of farms according to which quartile of the Principal Component 1 they occupied. Further research is recommended, but results suggest that welfare assessments incorporating many diverse measures remain necessary to provide sufficient detail about dairy herd welfare.

Investigating the Use of Dry Matter Intake and Energy Balance Prepartum as Predictors of Digestive Disorders Postpartum

One objective was to evaluate the association of dry matter intake as a percentage of body weight (DMI%BW) and energy balance (EB) prepartum and postpartum, and energy-corrected milk (ECM) postpatum with digestive disorders postpartum. For this, ANOVA was used, and DMI%BW, EB, and ECM were the outcome variables, and left displaced abomasum (LDA), indigestion, and other digestive disorders (ODDZ) were the explanatory variables. The main objective was to evaluate prepartum DMI%BW and EB as predictors of digestive disorders. For this, logistic regression was used, and LDA, indigestion, and ODDZ were the outcome variables and DMI%BW and EB were the explanatory variables. Data from 689 cows from 11 experiments were compiled. Left displaced abomasum was not associated with prepartum DMI%BW or EB. Postpartum data were normalized to the day of the event (day 0). Cows that developed LDA had lesser postpartum DMI%BW on days −24, −23, −12, −7 to 0 and from days 1 to 8, 10 to 12, and 14 and 16, lesser postpartum EB from days −7 to −5, −3 to 0, and 12, and lesser postpartum energy-corrected milk on days −19, −2, −1, 0, 7, 9, 10, 15, and 17 relative to diagnosis than cows without LDA. Cows that developed indigestion had lesser prepartum DMI%BW and EB than cows without indigestion, and lesser postpartum DMI%BW on days −24, −1, 0, 1, and 2, and greater DMI%BW on day 26, lesser ECM on days −24, −2, −1, 0, 1, and 2 relative to diagnosis. Postpartum EB was not associated with indigestion postpartum. Cows that developed ODDZ had lesser prepartum DMI%BW on day −8 and from days −5 to −2, lesser prepartum EB on day −8 and from days −5 to −2, and lesser postpartum DMI%BW than cows without ODDZ. Each 0.1 percentage point decrease in the average DMI%BW and each Mcal decrease in the average EB in the last 3 days prepartum increased the odds of having indigestion by 9% each. Cutoffs for DMI%BW and EB during the last 3 days prepartum to predict indigestion were established and were ≤1.3%/day and ≤0.68 Mcal/day, respectively. In summary, measures of prepartum DMI%BW and EB were associated with indigestion and ODDZ postpartum and were predictors of indigestion postpartum, although the effect sizes were small.

Training and Validating a Machine Learning Model for the Sensor-Based Monitoring of Lying Behavior in Dairy Cows on Pasture and in the Barn

Simple Summary

There are various systems available for health monitoring and heat detection in dairy cows. By continuously monitoring different behavioral patterns (e.g., lying, ruminating, and feeding), these systems detect behavioral changes linked to health disorders and estrous. Most of the systems were developed for cows kept indoors, and only a few systems are available for pasture-based farms. The systems developed for the barn failed to detect the targeted behavior and thereby its changes on the pasture and vice versa. Therefore, our goal was to train and validate a machine learning model for the automated prediction of lying behavior in dairy cows kept on pastures, as well as indoors. Data collection was conducted on three dairy farms where cows were equipped with the collar-based prototype of the monitoring system and recorded with cameras in parallel. The derived dataset was used to develop the machine learning model. The model performed well in predicting lying behavior in dairy cows both on the pasture and in the barn. Therefore, the building of the model presents a successful first step towards the development of a monitoring system for dairy cows kept on pasture and in the barn.

Abstract

Monitoring systems assist farmers in monitoring the health of dairy cows by predicting behavioral patterns (e.g., lying) and their changes with machine learning models. However, the available systems were developed either for indoors or for pasture and fail to predict the behavior in other locations. Therefore, the goal of our study was to train and evaluate a model for the prediction of lying on a pasture and in the barn. On three farms, 7–11 dairy cows each were equipped with the prototype of the monitoring system containing an accelerometer, a magnetometer and a gyroscope. Video observations on the pasture and in the barn provided ground truth data. We used 34.5 h of datasets from pasture for training and 480.5 h from both locations for evaluating. In comparison, random forest, an orientation-independent feature set with 5 s windows without overlap, achieved the highest accuracy. Sensitivity, specificity and accuracy were 95.6%, 80.5% and 87.4%, respectively. Accuracy on the pasture (93.2%) exceeded accuracy in the barn (81.4%). Ruminating while standing was the most confused with lying. Out of individual lying bouts, 95.6 and 93.4% were identified on the pasture and in the barn, respectively. Adding a model for standing up events and lying down events could improve the prediction of lying in the barn.

Motivations and attitudes of Brazilian dairy farmers regarding the use of automated behaviour recording and analysis systems

In this Research Communication we investigate the motivations of Brazilian dairy farmers to adopt automated behaviour recording and analysis systems (ABRS) and their attitudes towards the alerts that are issued. Thirty-eight farmers participated in the study distributed into two groups, ABRS users (USERS, n = 16) and non-users (NON-USERS, n = 22). In the USERS group 16 farmers accepted being interviewed, answering a semi-structured interview conducted by telephone, and the answers were transcribed and codified. In the NON-USERS group, 22 farmers answered an online questionnaire. Descriptive analysis was applied to coded answers. Most farmers were young individuals under 40 years of age, with undergraduate or graduate degrees and having recently started their productive activities, after a family succession process. Herd size varied with an overall average of approximately 100 cows. Oestrus detection and cow's health monitoring were the main reasons given to invest in this technology, and cost was the most important factor that prevented farmers from purchasing ABRS. All farmers in USERS affirmed that they observed the target cows after receiving a health or an oestrus alert. Farmers believed that they were able to intervene in the evolution of the animals' health status, as the alerts gave a window of three to four days before the onset of clinical signs of diseases, anticipating the start of the treatment.The alerts issued by the monitoring systems helped farmers to reduce the number of cows to be observed and to identify pre-clinically sick and oestrous animals more easily. Difficulties in illness detection and lack of definite protocols impaired the decision making process and early treatment, albeit farmers believed ABRS improved the farm's routine and reproductive rates.

Predictive models to identify Holstein cows at risk of metritis and clinical cure and reproductive/productive failure following antimicrobial treatment

Precision dairy farming, specifically the design of management strategies according to the animal's needs, may soon become the norm since automated technologies that generate large amounts of data for each individual are becoming more affordable. Our objectives were to determine whether the use of behavioral changes could improve the accuracy of prediction of the risk of metritis and the risk of clinical cure of cows diagnosed with metritis. Addition of behavioral data to the algorithms to predict the outcomes of interest increased their accuracy by 7 to 32%. The incidence of metritis in postpartum dairy cows ranges from 20 to 40%. Unfortunately, approximately 30% of cows treated with antimicrobials following the diagnosis of metritis fail to cure and have impaired reproductive performance. Automated behavior monitoring devices have become more affordable and accessible. In the current study, we investigated whether behavioral changes recorded by automated devices improve models for the prediction, within 42 h of calving, of metritis and acute metritis. Furthermore, we determined whether behavioral changes aid on the prediction, 24 h before the diagnosis of metritis, of cure in response to antimicrobial treatments and the reproductive (failure to become pregnant)/productive (bottom quartile of milk yield) success within 200 d in milk (DIM). At enrollment, Holstein cows (n = 555) from two farms were fitted with an automated device (HR-LDn tag, SCR Engineers Ltd., Netanya, Israel) 21 d before the expected calving date. Cows were examined for metritis (fetid, watery, red/brown uterine discharge) and were randomly assigned to receive ampicillin trihydrate or ceftiofur crystalline free acid treatments. Contemporary cows with no clinical diseases (NoCD = 362) were paired with cows with metritis. Cure from metritis was defined as the absence of fetid, watery, pink/brown uterine discharge and rectal temperature < 39.5 °C, 11 d after diagnosis. In addition, cows in the lowest quartile of milk production, within lactation and farm, and that were not pregnant by 200 DIM were classified as failure. We built models containing: routinely-available data [lactation number (1, 2, ≥3), calf sex, still birth, twining, dystocia, vaginal laceration score, days on the close-up diets], body condition score (BCS) and BCS change from enrollment to calving (ΔBCS), behavior (feeding, rumination, idle, and active time), and their interactions. The area under the curve (AUC) of the models containing routinely-available data, ΔBCS, and behavior data at 2 DIM to predict metritis [AUC = 0.82, 95% confidence interval (CI) = 0.78, 0.85] and acute metritis (AUC = 0.87, 95% CI = 0.83, 0.89) were (P < 0.01) excellent; whereas the models predicting cure (AUC = 0.92, 95% CI = 0.85, 0.95) and failure (AUC = 0.90, 95% CI = 0.84, 0.94) were outstanding. Behavioral changes peripartum contribute for the identification of cows at risk for metritis, allowing the development of preventive strategies. In addition, predicting whether cows will respond to antimicrobial treatment and succeed during lactation may allow for earlier decision-making regarding treatment and culling.

Potential Benefits to Dairy Cow Welfare of Using a Ceftiofur-Ketoprofen Combination Drug for the Treatment of Inflammatory Disease Associated with Pyrexia: A Field Clinical Trial on Acute Puerperal Metritis

Simple Summary

Some diseases of dairy cows require the use of an antimicrobial and an anti-inflammatory drug in association to be fully cured and relieve pain. However, pharmacological treatments in cattle are subject to strict regulations and restrictions, and cow handling is not always easy and safe. For these reasons, only the antimicrobial is often administered, thus not fully applying the appropriate therapeutic protocol. This study investigated whether the use of a drug combining ceftiofur and ketoprofen in a single injection instead of ceftiofur alone can improve the healing and welfare of dairy cows affected by a pyretic inflammatory disease, such as acute puerperal metritis. The results show that the variation in the physiological parameters was similar between the two treatment groups, and daily activity and milk yield did not differ from healthy cows. However, affected cows that were treated with the combined drug seemed to be more likely to become pregnant within 120 days in milk than those that received the antimicrobial alone, having an estimated number of days open more similar to that of the healthy cows.

Abstract

This study aimed at investigating the benefits of using a drug combining ceftiofur and ketoprofen in a single injection on dairy cow welfare in the case of inflammatory disease with pyrexia, such as acute puerperal metritis (APM). Cows of an Italian dairy farm were examined between 5 and 14 days of calving: those with APM were randomly treated either with combined ceftiofur–ketoprofen (CD) or ceftiofur alone (C), starting from Day 0, and an equal number of healthy cows served as a control (CTR). Clinical examination and blood sampling were performed until Day 7 in each group according to specific schedules. Daily cow activity was recorded until Day 14 and daily milk production until Day 30. Additional data on fertility were collected until 120 days in milk (DIM). Data of 20 cows per group were analyzed. Body temperature and haptoglobin concentration dropped between Day 0 and 4 in both CD and C, approaching the level of CTR. The cure rate at Day 7 (body temperature < 39.0 °C) was 65 (CD) and 55% (C), without statistical difference. Neither cow activity nor milk production differed among the three groups. Reproductive performances in both CD and C were similar to CTR, but CD cows were 2.8 times more likely to be pregnant within 120 DIM than C, becoming pregnant about 14 days sooner. Both treatments (CD and C) have been effective in bringing the cows back to health conditions (CTR), and further studies would be needed to confirm the positive effect observed for CD on days open of the affected cows.

Unraveling the Relationship between Milk Yield and Quality at the Test Day with Rumination Time Recorded by a PLF Technology

Simple Summary

Precision livestock farming, by real time monitoring of dairy cows, has the potential to generate a huge amount of data to be used for farm management purposes, as well as in breeding programs. Daily rumination time (RT) recorded by commercial systems is promising in this context because it may be related to individual milk yield and composition. However, it is necessary to assess the ability of sensor data to be used in a predictive model, but also to evaluate and standardize the correct phenotypes, and how they are related to individual variability rather than from other sources. RT data and milk test day (TD) records collected from 691 cows, monitored for thirteen months, were analyzed for the already mentioned goals and to better characterize the effect of high-, medium- and low-level daily RT on milk yield and composition. Our results showed that “animal” in a farm major contributed to the RT total variability, confirming a possible use in breeding program. The higher RT class reported the best productive performance for milk and each solid yield, in spite of a small reduction in their contents, and appears to be related to a higher degree of saturation in the fatty acid profile.

Abstract

The study aimed to estimate the components of rumination time (RT) variability recorded by a neck collar sensor and the relationship between RT and milk composition. Milk test day (TD) and RT data were collected from 691 cows in three farms. Daily RT data of each animal were averaged for 3, 7, and 10 days preceding the TD date (RT_D). Variance component analysis of RT_D, considering the effects of farm, cow, parity, TD date, and lactation phase, showed that a farm, followed by a cow, had major contributions to the total variability. The RT₁₀ variable best performed on TD milk yield and quality records across models by a multi-model inference approach and was adopted to study its relationship with milk traits, by linear mixed models, through a 3-level stratification: low (LRT₁₀ ≤ 8 h/day), medium (8 h/day < MRT₁₀ ≤ 9 h/day), and high (HRT₁₀ > 9 h/day) RT. Cows with HRT₁₀ had greater milk, fat, protein, casein, and lactose daily yield, and lower fat, protein, casein contents, and fat to protein ratio compared to MRT₁₀ and LRT₁₀. Higher percentages of saturated fatty acid and lower unsaturated and monounsaturated fatty acid were found in HRT₁₀, with respect to LRT₁₀ and MRT₁₀ observations.

Assessment of the Relationship between Postpartum Health and Mid-Lactation Performance, Behavior, and Feed Efficiency in Holstein Dairy Cows

The objective of this study was to investigate the relationships between postpartum health disorders and mid-lactation performance, feed efficiency, and sensor-derived behavioral traits. Multiparous cows (n = 179) were monitored for health disorders for 21 days postpartum and enrolled in a 45-day trial between 50 to 200 days in milk, wherein feed intake, milk yield and components, body weight, body condition score, and activity, lying, and feeding behaviors were recorded. Feed efficiency was measured as residual feed intake and the ratio of fat- or energy-corrected milk to dry matter intake. Cows were classified as either having hyperketonemia (HYK; n = 72) or not (n = 107) and grouped by frequency of postpartum health disorders: none (HLT; n = 94), one (DIS; n = 63), or ≥2 (DIS+; n = 22). Cows that were diagnosed with HYK had higher mid-lactation yields of fat- and energy-corrected milk. No differences in feed efficiency were detected between HYK or health status groups. Highly active mid-lactation time was higher in healthy animals, and rumination time was lower in ≥4th lactation cows compared with HYK or DIS and DIS+ cows. Differences in mid-lactation behaviors between HYK and health status groups may reflect the long-term impacts of health disorders. The lack of a relationship between postpartum health and mid-lactation feed efficiency indicates that health disorders do not have long-lasting impacts on feed efficiency.

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.

Association of days in close up, gestation length, and rumination around time of calving with disease and pregnancy outcomes in multiparous dairy cows

The purpose of this study was to evaluate the effect of rumination times and days spent in a close-up group before calving (DCU) on early-lactation health and reproductive outcomes in dairy cows. Data were gathered for 719 cows located in a single herd. Herd management and reproductive records were analyzed for cows receiving treatment in the first 30 d of lactation (days in milk; DIM) for clinical mastitis, reproductive tract disease, ketosis, milk fever, and displaced abomasum. Rumination times for each cow were downloaded daily from the herd's automated collar system used to generate heat and health alerts for each cow beginning at 21 d precalving until 14 d postcalving. During the first 30 DIM, 121 cows (18%) developed at least 1 disease-any combination of ketosis (40 cows, 5.9% of total), mastitis (17 cows, 2.5%), metritis (75 cows, 11%), milk fever (17 cows, 2.5%), or displaced abomasum (28 cows, 4.1%); 305 cows (45%) were pregnant again at 100 DIM, and an additional 139 cows (20%) were pregnant at 150 DIM. Principal component analysis was used to determine the relationship between gestation length and DCU and their association with the odds of developing disease in early lactation. We did not find any significant association between precalving rumination time and disease within the first 30 DIM. Higher rumination time in the week before calving was shown to be strongly linked to a shorter time to subsequent pregnancy, whereas rumination times postcalving were not associated with changes in the time to pregnancy. Principal component analysis showed that a curvilinear combination of gestation length and DCU (principal component 1) was significantly associated with changes in disease incidence in the first 30 DIM. Gestation length and time spent in close up are important management factors in reducing the incidence of disease in early lactation, and rumination times around calving may help predict future reproductive outcomes.

Effect of oral calcium administration on metabolic status and uterine health of dairy cows with reduced postpartum rumination and eating time

Background

Hypocalcemia has detrimental effects on health and performance of dairy cows. As hypocalcemic cows show reduced feed intake, we hypothesized that cows with reduced combined rumination and eating time (CRET) may benefit from Ca supplementation. The objective was to evaluate the effect of postpartum oral Ca administration on metabolic status (Calcium [Ca], fatty acids [FA], and β-Hydroxybutyrate [BHB] serum concentrations) and incidence of puerperal metritis (PM) in dairy cows with reduced postpartum CRET. Cows in an organic-certified dairy, diagnosed with reduced CRET (< 489 min/d; n = 88) during the first day postpartum were assigned into 1 of 2 treatments: i) Calcium administration (CA; n = 45) that received 1 Ca oral capsule (Bovikalc bolus, Boehringer Ingelheim, St. Joseph, MO) containing CaCl2 and CaSO4 (43 g of Ca) once per day, for 3 consecutive days, starting at d 1 postpartum; and ii) Control (CON; n = 43) that did not receive oral Ca. A convenience group consisting of cows with CRET ≥489 min/d was used for comparison and did not receive oral Ca (NOR; n = 96).

Results

At day 1 postpartum cows with reduced CRET had lower Ca serum concentrations (CA = 2.08 mmol/L; CON = 2.06 mmol/L) compared with NOR cows (2.17 mmol/L). Calcium concentrations at d 3, 5, and 12 postpartum were not different among the three groups. Serum FA concentrations at d 1, 3 and 5 postpartum were higher in both CA and CON cows compared with NOR. At d 12, only CA cows had higher FA concentrations than NOR cows. Serum BHB concentrations at d 3 were highest in CA, with no difference between CON and NOR. At d 5, BHB concentrations were higher in CA, followed by CON, and NOR. No effect was observed for Ca administration on incidence of PM and reproductive performance. CON cows had lower survival at 30 DIM (86.5%) than NOR cows (97.9%).

Conclusions

The use of remote sensor technology identified cows with reduced rumination and eating time that had lower postpartum serum concentrations of calcium and altered metabolic status. However, oral calcium administration to cows with reduced CRET did not affect incidence of metabolic disorders nor reproductive health and subsequent pregnancy. Although survival at 30 days postpartum was lower for non-Ca supplemented cows, the identification of effective interventions in cows with reduced CRET requires further consideration.

Trends in Changes of Automatic Milking System Biomarkers and Their Relations with Blood Biochemical Parameters in Fresh Dairy Cows

The aim or this study was to determine the relationship between non-esterified fatty acids and biomarkers from an automatic milking system (AMS). Fresh dairy cows (n = 102) were selected and milked in Lely Astronaut® A3 milking robots. The rumination time (RT), body weight (BW), milk content and composition parameters, milk fat/protein ratio (F/P), and milk electrical conductivity were registered by the same milking robots. For examining non-esterified fatty acids (NEFAs), blood samples were acquired from cows in the dry period. According to the NEFA concentrations, all cows were divided into two groups: Group I, with <0.300 mEq/L (n = 66), and Group II, with ≥0.300 mEq/L (n = 36). Albumin (ALB), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), and cortisol concentrations were also analyzed once a week up to 30 days in milking. The study revealed that the cows in Group I had higher concentrations of ALB, cortisol, and GGT, but the average concentration of AST was lower. In Group 1, the milk F/P was higher, but the milk yield was lower. We hypothesize that biomarkers from AMS could help in the early diagnosis of metabolic diseases after calving or to control negative energy balance before calving.

Rumination time around dry-off relative to the development of diseases in early-lactation cows

Monitoring rumination time (RT) around the time of calving is an effective way of identifying cows at risk of disease in early lactation. However, this only allows for the identification of cows a few days before the onset of clinical signs; thus, effective preventive measures cannot be implemented. Recent research has suggested that biomarkers of immune and metabolic function measured at dry-off (DO) can predict higher disease risk in early lactation. Nevertheless, the extent to which RT around DO is associated with early-lactation disease risk remains unexplored. Thus, the objective of this study was to compare RT in the weeks before and after DO between cows that did and did not experience health disorders in early lactation. For this, we conducted an observational retrospective cohort study utilizing the records available from a large commercial dairy herd in which RT is recorded daily using an automated system. Daily RT from -7 to +14 d relative to DO from 2,258 DO cycles and their respective health records in the first 60 d in milk were used. Differences in RT between animals with and without a disease history were tested with the Student t-test with Bonferroni adjustment. Mixed linear regression analyses were performed to assess differences in RT around DO and the association of RT with the occurrence of mastitis, metritis, retained placenta, hyperketonemia, lameness, hypocalcemia, pneumonia, and displaced abomasum. Rumination time decreased abruptly at DO and remained lower for 3 to 4 d compared with the days before DO. On average, cows affected by hyperketonemia and lameness ruminated 9.83 ± 6.40 and 15.00 ± 6.08 min/d less than unaffected cows, respectively. Cows that developed lameness in the first 60 d in milk showed reduced RT from 1 to 3 d following DO compared with cows that were not diagnosed with lameness in early lactation. However, RT around DO was not associated with the occurrence of the other health disorders studied here. Our results demonstrate that DO is a stressful event for dairy cows resulting in decreased RT for several days. Furthermore, the association between RT around DO and some early-lactation diseases suggests that RT could be a useful tool to identify at-risk cows early enough to allow for preventive interventions. Further studies should investigate the diagnostic utility of incorporating RT data early in the dry period in the disease prediction algorithms of rumination sensors.