Automatic monitoring of the health and metabolic status of dairy cows

A time series model of daily milk yields and its possible use for detection of a disease (ketosis)

A time series model was used to describe the daily milk yield of healthy cows in the first 48 days of lactation. A moving average (MA) model of order 1 on the first-order differences of the data was selected — the same chosen in a previous study by Deluyker et al. (1990). When the model was used to generate predicted daily yields in a second data set, for cows in which clinical ketosis had been diagnosed, it was found that significant deviations of actual yield below the daily forecast occurred from 3 days before the day of diagnosis. The model appeared to be transportable to healthy cows from another herd. Threshold values were defined to identify ailing animals by their deviation from predicted yield. However, the thresholds were not very sensitive, and required that a fairly high level of false positives be accepted (25% of a healthy herd over a 5-day period).

Animal board invited review: precision livestock farming for dairy cows with a focus on oestrus detection

Dairy cows are high value farm animals requiring careful management to achieve the best results. Since the advent of robotic and high throughput milking, the traditional few minutes available for individual human attention daily has disappeared and new automated technologies have been applied to improve monitoring of dairy cow production, nutrition, fertility, health and welfare. Cows milked by robots must meet legal requirements to detect healthy milk. This review focuses on emerging technical approaches in those areas of high cost to the farmer (fertility, metabolic disorders, mastitis, lameness and calving). The availability of low cost tri-axial accelerometers and wireless telemetry has allowed accurate models of behaviour to be developed and sometimes combined with rumination activity detected by acoustic sensors to detect oestrus; other measures (milk and skin temperature, electronic noses, milk yield) have been abandoned. In-line biosensors have been developed to detect markers for ovulation, pregnancy, lactose, mastitis and metabolic changes. Wireless telemetry has been applied to develop boluses for monitoring the rumen pH and temperature to detect metabolic disorders. Udder health requires a multisensing approach due to the varying inflammatory responses collectively described as mastitis. Lameness can be detected by walk over weigh cells, but also by various types of video image analysis and speed measurement. Prediction and detection of calving time is an area of active research mostly focused on behavioural change.

Methods and on-farm devices to predict calving time in cattle

In livestock farming, accurate prediction of calving time is a key factor for profitability and animal welfare. The most accurate and sensitive methods to date for prediction of calving within 24 h are the measurement of pelvic ligament relaxation and assays for circulating progesterone and oestradiol-17β. Conversely, the absence of calving within the next 12-24 h can be accurately predicted by the measurement of incremental daily decrease in vaginal temperature and by the combination of pelvic ligament relaxation and teat filling estimates. Continuous monitoring systems can detect behavioural changes occurring on the actual day of calving, some of them being accentuated in the last few hours before delivery; standing/lying transitions, tail raising, feeding time, and dry matter and water intakes differ between cows with dystocia and those with eutocia. Use of these behavioural changes has the potential to improve the management of calving. Currently, four types of devices for calving detection are on the market: inclinometers and accelerometers detecting tail raising and overactivity, abdominal belts monitoring uterine contractions, vaginal probes detecting a decrease in vaginal temperature and expulsion of the allantochorion, and devices placed in the vagina or on the vulvar lips that detect calf expulsion. The performance of these devices under field conditions and their capacity to predict dystocia require further investigation.

An assessment of producer precision dairy farming technology use, prepurchase considerations, and usefulness

An online survey to identify producer precision dairy farming technology perception was distributed in March 2013 through web links sent to dairy producers through written publications and e-mail. Responses were collected in May 2013 and 109 surveys were used in statistical analysis. Producers were asked to select parameters monitored by technologies on their farm from a predetermined list and 68.8% of respondents indicated technology use on their dairies (31.2% of producers not using technologies). Daily milk yield (52.3%), cow activity (41.3%), and mastitis (25.7%) were selected most frequently. Producers were also asked to score the same list of parameters on usefulness using a 5-point scale (1=not useful and 5=useful). Producers indicated (mean ± SE) mastitis (4.77±0.47), standing estrus (4.75±0.55), and daily milk yield (4.72±0.62) to be most useful. Producers were asked to score considerations taken before deciding to purchase a precision dairy farming technology from a predetermined list (1=not important and 5=important). Producers indicated benefit-to-cost ratio (4.57±0.66), total investment cost (4.28±0.83), and simplicity and ease of use (4.26±0.75) to be most important when deciding whether to implement a technology. Producers were categorized based on technology use (using technology vs. not using technology) and differed significantly across technology usefulness scores, daily milk yield (using technologies: 4.83±0.07 vs. not using technologies: 4.50±0.10), and standing estrus (using technologies: 4.68±0.06 vs. not using technologies: 4.91±0.09). The same categories were used to evaluate technology use effect on prepurchase technology selection criteria and availability of local support (using technologies: 4.25±0.11 vs. not using technologies: 3.82±0.16) differed significantly. Producer perception of technology remains relatively unknown to manufacturers. Using this data, technology manufacturers may better design and market technologies to producer need.

Heart rate and heart rate variability in multiparous dairy cows with unassisted calvings in the periparturient period

Behavioural changes before calving can be monitored on farms; however, predicting the onset of calving is sometimes difficult based only on clinical signs. Heart rate (HR) and heart rate variability (HRV) as non-invasive measures of autonomic nervous system (ANS) activity were investigated in Holstein-Friesian cows (N=20) with unassisted calvings in the periparturient period to predict the onset of calving and assess the stress associated with calving. R-R-intervals were analysed in 5-min time windows during the following three main periods of measurement: 1) between 0 and 96 h before the onset of calving restlessness (prepartum period); 2) during four stages of calving: (I) early first stage; between the onset of calving restlessness and the first abdominal contractions; (II) late first stage (between the first abdominal contractions and the appearance of the amniotic sac); (III) early second stage (between the appearance of the amniotic sac and the appearance of the foetal hooves); (IV) late second stage (between the appearance of the foetal hooves and delivery of the calf), and 3) over 48 h following calving (postpartum period). Data collected between 72 and 96 h before calving restlessness was used as baseline. Besides HR, Poincaré measures [standard deviation 1 (SD1) and 2 (SD2) and SD2/SD1 ratio], the root mean square of successive differences (RMSSD) in R-R intervals, the high-frequency (HF) component of HRV and the ratio between the low-frequency (LF) and the HF components (LF/HF ratio) were calculated. Heart rate increased only following the onset of the behavioural signs, peaked before delivery of the calf, then decreased immediately after calving. Parasympathetic indices of HRV (RMSSD, HFnorm and SD1) decreased, whereas sympathovagal indices (LF/HF ratio and SD2/SD1 ratio) increased significantly from baseline between 12 and 24 before the onset of calving restlessness. The same pattern was observed between 0 and 1h before calving restlessness. Following the onset of behavioural signs, parasympathetic activity increased gradually with a parallel shift in sympathovagal balance towards parasympathetic tone, which was possibly a consequence of oxytocin release, which induces an increase in vagus nerve activity. Parasympathetic activity decreased rapidly between 0 and 0.5h following calving and was lower than measured during all other stages of the study, while sympathetic activity peaked during this stage and was higher than measured during any other stages. Between 0 and 4h after calving vagal tone was lower than baseline, whereas sympathovagal balance was higher, reflecting a prolonged physiological challenge caused by calving. Vagal activity decreased, whereas sympathovagal balance shifted towards sympathetic tone with increased live body weight of the calf during the late second stage of calving, suggesting higher levels of stress associated with the higher body weight of calves. All HRV indices, measured either at the late second stage of calving and between 12 and 24h after calving, were affected by the duration of calving. Our results indicate that ANS activity measured by HRV indices is a more immediate indicator of the onset of calving than behaviour or HR, as it changed earlier than when restlessness or elevation in HR could be observed. However, because of the possible effects of other physiological mechanisms (e.g. oxytocin release) on ANS activity it seems to be difficult to measure stress associated with calving by means of HRV between the onset of calving restlessness and delivery. Further research is needed to enable more precise interpretation of the prepartum changes in HR and HRV in dairy cattle.

Fibrinogen and ceruloplasmin in plasma and milk from dairy cows with subclinical and clinical mastitis

The potential using of Acute Phase Proteins (APPs) in the assessment of mammary gland health was studied by examining the levels of Fibrinogen (Fb) and Ceruloplasmin (Cp) in plasma and milk from dairy cows with different grades of mastitis. Plasma samples were taken from jugular vein and milk samples were collected from quarters of cows with subclinical and clinical mastitis, as well as healthy controls. California Mastitis Test (CMT) were performed on each udder quarter of cows for detection of CMT2+ and CMT3+ quarters. CMT (0) and culture negative cases were considered healthy cows. Clinical mastitis, was graded as mild (clots in milk) or moderate (clots in milk and visible signs of inflammation in the mammary gland/s). The concentrations of Fb in the plasma of the cows with subclinical and clinical mastitis were higher than in the plasma of the healthy cows (p<0.01). There was no significant difference in plasma concentration of Cp between healthy and subclinical groups (p>0.05), but differences between clinical and healthy groups were significant (p<0.05). The concentrations of Fb and Cp in the milk of the cows with subclinical and clinical mastitis were higher than in the milk of the healthy cows (p<0.01). The results indicated that measurement of Fb in plasma and milk and Cp only in milk might be suitable for early diagnosis of mastitis in dairy cows.

Milk Odd- and Branched-Chain Fatty Acids in Relation to the Rumen Fermentation Pattern

The objectives of this study were 1) to determine whether a relationship exists between molar proportions of volatile fatty acids in the rumen and milk odd-and branched-chain fatty acid concentrations (i.e., iso C13:0, anteiso C13:0, iso C14:0, C15:0, iso C15:0, anteiso C15:0, iso C16:0, C17:0, iso C17:0, anteiso C17:0, and cis-9 C17:1); and 2) to evaluate the accuracy of prediction of the latter equations using an independent data set. For development of the regression equations, individual cow data from 10 feeding experiments with rumen-fistulated dairy cows were used, resulting in a data set of 148 observations. Milk odd- and branched-chain fatty acids were closely related to the molar proportions of acetate (SE = 15.3 mmol/mol), propionate (SE = 14.7 mmol/mol), and butyrate (SE = 9.2 mmol/mol). These regression equations were further validated using data from the literature (n = 14). Evaluation of these prediction equations using the independent data set resulted in a root mean square prediction error of 3.0, 9.0, and 8.9% of the observed mean for acetate, propionate, and butyrate, respectively. In addition, less then 5% of the mean square prediction error was due to line bias. This suggests that the currently developed prediction equations based on milk odd- and branched-chain fatty acids show potential to predict molar proportions of individual volatile fatty acids in the rumen.

The use of test day information to predict energy intake of dairy cows in early lactation

This study aimed to validate a previously developed model for the estimation of energy balance in high producing dairy cows from test day information during the first 12 wk of lactation. Monensin (an ionophor) increases the energy status of dairy cows. Gold standard for the validation was a higher energy status, indicated by lower blood ketone body concentrations, lower percent milk fat, and higher milk-yield of monensin-supplemented than control cows in 8 randomized block design feeding trials. Estimated energy intake (eE(intake)) was calculated as estimated energy balance (eEB) plus energy in actual milk produced (in units of MJ(nel)) plus a constant or variable amount of energy required for maintenance. The variable amount was based on BW, while the constant was the average BW in each parity group (1, 2, 3, 4+). Both eEB and eE(intake) were compared between groups of cows with and without monensin supplementation (n = 600 lactations). The trials started with a presupplement period during lactation wk 2 to 5 followed by a supplementation period during lactation wk 6 to 12. During the presupplement period, both eEB and eE(intake) were similar for all cows. At 2, 3, and 8 wk after starting the monensin supplementation, the eEB of the supplemented cows was significantly higher, while eE(intake) was significantly higher throughout the supplementation period. The results were similar for the 2 methods of calculating energy for maintenance, variable or constant. The feed conversion efficiency, calculated as kg of fat-protein corrected milk per MJ(nel) of eE(intake), was highest in first calving cows compared with cows having more lactations, and correlated with standard milk production at trial group level. It was concluded that eE(intake) was a valid measure of net energy absorption.

Diurnal variation and the effect of feed restriction on plasma and milk metabolites in TMR-fed dairy cows

The objective was to study the diurnal variation in metabolites in plasma and milk of dairy cows fed total mixed rations (TMR) with a low-energy (LE) or high-energy content (HE) expected to give a minor and a major diurnal variation, respectively. Further, the purpose was to quantify and compare the responses in plasma and milk parameters when cows changed from ad libitum to restrictive feeding. Eight multiparous, early-lactating Danish Holstein cows were used in a cross-over design with two consecutive 14-day periods. Blood and milk samples were collected hourly on day 11 of each period and on days 12-14 of each period, the cows were fed restrictively (65% of ad libitum dry-matter intake). The concentration of beta-hydroxybutyrate (BHB) in plasma was significantly higher in the evening for cows fed the HE TMR, than for cows fed the LE TMR. There was a significant diurnal variation in BHB in milk, with the highest concentrations between milkings and the lowest concentrations at milking. Non-esterified fatty acids (NEFA) in plasma showed significant diurnal variation that was caused by high concentrations in the morning. Plasma glucose did not show any diurnal variation. It has been argued that feeding a TMR removes diurnal changes related to feeding, which is contrary to earlier diurnal studies where concentrates have been fed twice daily. Feed restriction increased (P < 0.001) NEFA and BHB in plasma by 121 and 90%, respectively, while the glucose concentration decreased (P < 0.001) by 19%. Milk concentrations of BHB, citrate and fat increased (P < 0.001) by 163, 11 and 26%, respectively, because of feed restriction, while there were no changes in milk protein and lactose. The relatively high increase in BHB during feed restriction suggests that BHB is more advantageous as a milk indicator of metabolic status in dairy cows than citrate and fat.

Sensors and management support in high-technology milking

Two directions can be distinguished in the development of high-tech milking equipment: 1) high-capacity milking parlors with a high throughput of cows per person per hour and 2) automatic milking systems in which manual labor is replaced by a milking robot. High-capacity milking parlors are developed in such a way that one operator is able to milk many cows, partly by automation and partly by optimization of available labor. In such parlors, one operator can milk up to 125 cows per hour. This means that there are only a few seconds available for udder preparation. In an automatic milking system, a robot takes over all manual labor during milking. Currently available systems have one robot arm working with one milking stall (one-stall system) or one robot arm working with more milking stalls (multiple-stall systems). Cows have to go to the automatic milking system voluntarily. Therefore, there is a large variation in milking intervals. Moreover, a large variation between milkings and between cows was observed in milk flow rate, machine-on time and udder preparation time. Both developments in high-tech milking have effects on the milk ejection. The small amount of time dedicated to udder preparation in high-capacity milking parlors has negative effects on the milk ejection, among others leading to more bimodal milk flow curves and longer machine-on time. In automatic milking systems, the variation in time between udder preparation and cluster attachment and in milking frequency might have an effect on milk ejection. Lactation physiology can play a role in solving the questions around milk ejection in high-tech milking systems. The introduction of high-tech milking systems makes decision support systems using sensors necessary. These systems should assist in detection of abnormal milk and mastitis. To a lesser extent, diseased cows need to be brought to the attention of the dairy farmer. Some sensors are currently available for this purpose, but they do not fulfill all demands. In the near future other sensors might be developed. It is important that this development is demand driven and not technology driven. Lactation physiology can play an important role in the determination of milk components useful for automatic detection.

Acute phase proteins in serum and milk from dairy cows with clinical mastitis

The serum concentrations of haptoglobin, serum amyloid A and alpha1 acid glycoprotein were determined in serum collected from healthy dairy cows and cows with clinical mastitis, graded as mild (clots in milk) or moderate (clots in milk and visible signs of inflammation in the mammary gland/s) to assess their relative diagnostic value in detecting the disease. The concentrations of haptoglobin and serum amyloid A were also measured in milk collected from infected and uninfected quarters. The concentrations of haptoglobin and serum amyloid A were higher in the serum and milk from the cows with mild or moderate mastitis. The diagnostic value of haptoglobin in differentiating between healthy animals and those with mastitis gave sensitivities and specificities of 82 per cent and 94 per cent respectively with serum and 86 per cent and 100 per cent with milk. The diagnostic value of serum amyloid A in differentiating between healthy animals and those with mastitis gave sensitivities and specificities of 83 per cent and 90 per cent with serum and 93 per cent and 100 per cent with milk. The diagnostic value of serum alpha1 acid glycoprotein in differentiating between healthy animals and those with mastitis gave sensitivities and specificities of 62 per cent and 91 per cent.

Monitoring daily milk yields with a recursive test day repeatability model (Kalman filter)

Mixed model methodology and recursive estimation techniques (Kalman filter) were combined to detect significant changes in the performance level of both individual cows and an entire herd. Yields were predicted for the next time of recording using all data available up to that time. Predicted yields were compared with actual measurements. If the error of prediction, or innovation, exceeded +/- 2 times its standard deviation, the observation was considered to be significantly different from the former yield level. The data comprised 30,199 records for 135 cows and 366 d. Effects fitted in the model were test day, breed, and lactation class as fixed effects and cow within lactation number as a random effect. A lactation curve was fitted within lactation class. Of the observed milk yields, 9.2% deviated significantly from the expected value in a negative direction. None of the innovation of the fixed-day effect exceeded the threshold of two standard deviations. Compared with the results of rolling average, which were calculated as the average of a 10-d period, over 20% of the observations of the daily milk yield were classified differently by the two methods. The mixed-model method for recursive estimation takes better account of environmental and lactational effects influencing daily milk yields as the rolling average. The mixed-model recursive estimation method was applicable for the detection of suspicious (i.e., outside a specified prediction interval) observations of individual cows at the time of the actual recording.