Milk Electrical Conductivity Research Articles

Oestrus detection in dairy cows based on serial measurements using univariate and multivariate analysis

Abstract. As visual oestrus detection is difficult to perform in large herds, different technical devices were developed to facilitate oestrus detection. In this investigation the significance of the traits activity, milk yield, milk flow rate and electrical conductivity due to oestrus was analysed. The traits were recorded automatically during each milking on a commercial dairy farm. Oestrus detection was performed for 862 cows on basis of time series consisting of 15 days before oestrus, the day of oestrus and 15 days after oestrus. The day of oestrus was determined by the insemination which caused a calving after 265 to 295 days. The univariate analyses of traits were performed by the time series methods day-to-day comparison, moving average, exponential smoothing and Box-Jenkins three parameter smoothing. For multivariate analyses a fuzzy logic model was developed and modified for the different combinations of traits. The efficiency of the detection models and traits was determined by the parameters sensitivity, specificity and error rate. A moving average was the best suited time series method for oestrus detection by activity data. Sensitivity ranged between 94.2 and 71% and error rate was between 53.2 and 21.5% for threshold values between 40 and 120%. The traits milk yield, milk flow rate and electrical conductivity were not suitable for univariate oestrus detection. Depending on the considered traits multivariate analyses resulted in sensitivities between 87.0 and 87.9%. The error rate varied between 28.2 and 31.0%. Further analyses should include previous information such as time since last oestrus.

Application of the ‘tracking signal’ method to the monitoring of udder health and oestrus in dairy cows

Two time series models, namely, the moving average model of lag 14 (MAM) and the tracking signal method (TSM), have been used to detect oestrus and udder health in dairy cows based on activity data and electrical conductivity, respectively. The results show that the use of on-line activity data allowed us to obtain high values of sensitivity and specificity for oestrus detection irrespective of the model used. Regarding udder health, sensitivity and specificity values showed that the number of clinical and subclinical mastitis cases detected was similar for the two models, but the specificity value using TSM resulted in a significantly higher value than MAM. Thus, the TSM method could be considered advantageous because of reduction in the number of false positive in healthy cows. The results show that electrical conductivity data of mixed milk samples are useful to detect both clinical and subclinical mastitis with a good sensitivity, if the milk electrical conductivity on-line is correctly measured.

Detection model for mastitis in cows milked in an automatic milking system

Automated detection of diseases (such as mastitis) in dairy cows might be an alternative for detection by observation during milking — especially when using an automatic milking system (AMS). An outline of a detection model is given. This detection model includes time-series models for two variables (milk yield and electrical conductivity of milk), with interpolation on previous values. The model is flexible in the number of variables actually used. Parameter values and the residual variances are updated by linear regression after each milking. Alerts for mastitis are given when the residuals fall outside given confidence intervals. A data set with 111 cows for 16 months (on average, 58 lactating cows per day) was used to test the model. Depending on the chosen confidence interval, 42–44 out of 48 cases of clinical mastitis were detected; the remaining cases were not detected because not all data needed were available. These results were better than the results obtained with the model usually used on the farm. The number of false-positive alerts depended on the chosen confidence interval and was higher than the number found with the model usually used.

Application of Fuzzy Logic in Automated Cow Status Monitoring

Sensors that measure yield, temperature, electrical conductivity of milk, and animal activity can be used for automated cow status monitoring. The occurrence of false-positive alerts, generated by a detection model, creates problems in practice. We used fuzzy logic to classify mastitis and estrus alerts; our objective was to reduce the number of false-positive alerts and not to change the level of detected cases of mastitis and estrus. Inputs for the fuzzy logic model were alerts from the detection model and additional information, such as the reproductive status. The output was a classification, true or false, of each alert. Only alerts that were classified true should be presented to the herd manager. Additional information was used to check whether deviating sensor measurements were caused by mastitis or estrus, or by other influences. A fuzzy logic model for the classification of mastitis alerts was tested on a data set from cows milked in an automatic milking system. All clinical cases without measurement errors were classified correctly. The number of false-positive alerts over time from a subset of 25 cows was reduced from 1266 to 64 by applying the fuzzy logic model. A fuzzy logic model for the classification of estrus alerts was tested on two data sets. The number of detected cases decreased slightly after classification, and the number of false-positive alerts decreased considerably. Classification by a fuzzy logic model proved to be very useful in increasing the applicability of automated cow status monitoring.

DETECTION OF ESTRUS AND MASTITIS: FIELD PERFORMANCE OF A MODEL

A new detection model ( ‘ IMAG model ’ ) for estrus and mastitis in dairy cows was tested on four farms during several years. Such a test is necessary because information is lacking about the performance of detection models under field conditions. The test gave insight into the field performance of the IMAG model and the results were compared with the results of older models and with the results predicted by experts. Sensor data of milk yield, milk temperature, electrical conductivity of milk and animal activity were the inputs for the IMAG model. The IMAG model is based on time series analysis combined with a Kalman filter. This structure yields cow–dependent model parameters and combines data of different sensors. Results were compared with the manufacturer ’ s model (supplied with the sensors), based only on exponential smoothing on data from one sensor. The sensor equipment differed between farms. The sensitivity (percentage of estruses detected) for estrus varied from 63 to 80%, depending on the threshold used. Specificity (non–estruses not detected as estrus) varied from 94 to 98%. The sensitivity for clinical mastitis varied from 55 to 80%, depending on the threshold used. The specificity for mastitis varied from 94 to 99%. Significant differences existed between farms, in sensitivity for estrus and mastitis. The applied equipment could only partially explain the differences in estrus and mastitis detection results between farms. No relation between stage of lactation and activity level was found, although a lower activity level in the first period of lactation might be expected. The main conclusion is that a detection model can give good results, but only if the equipment is working properly. The new model outperformed the manufacturer ’ s model.

Variation in blood leucocytes, somatic cell count, yield and composition of milk of crossbred goats

Ten multiparous crossbred goats, five each of alpine × beetal (AB) and saanen × beetal (SB) were selected from the National Dairy Research Institute goat herd immediately after parturition. These were managed as per the practices followed in the institute’s goatherd. Blood and milk samples were collected at biweekly intervals from day 14 post-kidding for 22 weeks (154 days). Somatic cell count, electrical conductivity, fat, protein and lactose contents of milk were determined using standard methods. In the blood samples total leucocytes and differential leucocytes were also determined. Somatic cell counts were high immediately after parturition on day 14 of lactation and declined gradually with advanced lactation. There were individual variations ( P < 0.01) in somatic cell counts between different lactation periods. Somatic cell count of milk was negatively correlated with neutrophils only ( P < 0.05) and was neither correlated with milk yield, or with fat, protein, lactose content of milk. Electrical conductivity of milk was low up to four weeks of lactation and thereafter increased as the lactation advanced. Lactose content of milk declined gradually with the advancement of lactation. Fat content of milk was stable up to the eighth week and thereafter increased with advancement of lactation while the protein content of milk did not change significantly during lactation.

Dynamic programming to determine optimum investments in information technology on dairy farms

The feasibility of integrating the information about a number of information technology (IT) applications on dairy farms is gaining interest because of the potential to improve economic results. In this study optimal investment patterns involving five IT applications are evaluated: (1) automated concentrate feeding systems; (2) measurement of daily physical activity of cows; (3) in-line automated parlour systems recording milk production; (4) milk temperature; and (5) electrical conductivity of milk. The investment decisions are optimised by dynamic programming taking into account price reduction and technical progress over time. The objective is to determine the pattern value of (stepwise) IT investments that maximises the net present over a given planning horizon. Optimal investment patterns are calculated under different assumptions of technical progress, farm characteristics and farm scale. In this way it is determined under which conditions the applications under research would be incorporated. Results for a typical Dutch dairy farm show that the optimal investment pattern includes automated concentrate feeders. The incorporated electronic individual cow identification and hardware facilitates the appraisal of other in-line sensor investments such as activity measurement of cows.

Comparison of treatment of mastitis by oxytocin or antibiotics following detection according to changes in milk electrical conductivity prior to visible signs.

Mastitis was induced in dairy cows by infusion of 500 cfu of Streptococcus uberis into the mammary gland. Most infections developed to clinical disease, and the majority were predicted by changes in the electrical conductivity of the foremilk. The benefits of clinical prognosis and bacteriological cure were determined for cases that were treated when predicted to develop into clinical mastitis and compared with cases that were allowed to develop until milk clotted or until pyrexia before intramammary antibiotic treatment was used. Treatment prior to clinical mastitis included use of intramammary antibiotic or intramuscular oxytocin to allow stripping of residual milk to remove bacteria. All infections in which treatment was delayed resulted in clinical mastitis that was cured clinically and bacteriologically by sustained treatment using a broad-spectrum intramammary antibiotic preparation once daily but requiring a mean treatment time of 10 d. It was possible to prevent clinical mastitis from developing and to eliminate all infections in cows that were treated early when the developing disease was predicted by changes in the electrical conductivity of quarter foremilk and was treated aggressively by administering an intramammary antibiotic at each milking for 3 d. Treatment of 20 IU of oxytocin at six successive milkings of cows that were predicted to develop disease eliminated 25% of the infections, but 75% of the cows developed clinical mastitis. Those cases were resolved by sustained daily treatment using the same intramammary antibiotic. Elimination (100% clinical and bacteriological cure) of all infections caused by Strep. uberis was possible with early and aggressive or sustained use of the intramammary antibiotic. The early intervention using an intramammary antibiotic, when infection was first indicated by changes in the electrical conductivity of milk, was the most efficient method to achieve cure and led to quicker recovery of milk quality to a saleable standard.

The Effects of Early Antibiotic Treatment Following Diagnosis of Mastitis Detected by a Change in the Electrical Conductivity of Milk

Mastitis was induced experimentally by infusion of Streptococcus uberis or Staphylococcus aureus into the mammary glands of lactating dairy cows. Clinical mastitis was identified when clots appeared in foremilk (conventional diagnosis) or was predicted by changes in the electrical conductivity of foremilk (early diagnosis). The responses to intramammary antibiotic treatment that was initiated after early diagnosis of mastitis and after conventional diagnosis were compared. Early treatment significantly limited the severity of the disease and, in many cases, prevented the appearance of any visible signs of infection. Milk yield was less depressed, and the somatic cell count (SCC) was lower, when treatment was initiated earlier. The SCC of the quarter at the time mastitis was predicted was approximately 2 × 106 cells/ml for both pathogens, which was significantly less than when clots appeared at conventional diagnosis, approximately 4 × 106 and 12 × 106 cells/ml for Staph. aureus and Strep. uberis, respectively. The time required for SCC to recover to <4 × 105 cells/ml was significantly less, approximately half, for both pathogens following early detection and early initiation of treatment. When treatment was administered in response to early detection, the bacteriological and clinical cure was almost complete, and the amount of antibiotic used was ≤50% less. Obvious benefits for milk yield and quality and the health of the cow would result when changes in the electrical conductivity of milk are used to predict clinical mastitis and when treatment is initiated early.

Detection of Clinical Mastitis by Changes in Electrical Conductivity of Foremilk Before Visible Changes in Milk

Mastitis was induced by the direct infusion of Staphylococcus aureus or Streptococcus uberis into the mammary gland of lactating cows. Changes in electrical conductivity of foremilk indicated the establishment of bacteria, increased SCC, increased clotting of milk, and, hence, disease in advance of visible changes in the milk that could be diagnosed by a herdsperson. Clinical mastitis was detectable by changes in electrical conductivity of foremilk, 90% of cases were detectable when clots first appeared in foremilk, and 55% of cases were detectable up to 2 milkings prior to the appearance of clots. All subclinical infections from Staph. aureus were detected, but subclinical infections from Strep. uberis were not detected. The result suggested that clinical mastitis caused by these two major pathogens could e detected earlier by measuring changes in electrical conductivity of milk than by waiting for a herdsperson to detect visible changes in milk. Earlier detection would permit earlier treatment. However, the hand-held sensor used in this experiment is impractical for commercial application, and reliable automated sensors and decision-making algorithms are required.

Comparison of Analysis Techniques for On-Line Detection of Clinical Mastitis

Three techniques were compared for analysis of automatically collected data from the milking parlor. Mammary quarters showing signs of clinical mastitis were compared with randomly selected healthy quarters. Automatic data were analyzed from the milking on which the milkers observed clinical mastitis as well as data from the two prior milkings. Electrical conductivity of milk was not corrected for individual cows. Milking parlor data were preprocessed so that information on the electrical conductivity pattern during a milking was retained.Principal component analysis was used to verify whether variation in the data was caused by mastitis. Performance of logistic regression models for detection of clinical mastitis was compared with that of backpropagation neural networks.Variation in the quarter data was caused by mastitis. Automatic data from infected quarters did not always differ from data from healthy quarters, especially from the two prior milkings. The detection performance of the logistic regression model was similar to that of the neural networks. When both models were tested on the development data, sensitivity was approximately 75%, and specificity was approximately 90% at the milking of mastitis observation. Detection results were lower for the prior milkings. Therefore, not all incidences of clinical mastitis cases could be detected before clinical signs occurred.

Detection of Subclinical Mastitis from On-Line Milking Parlor Data

A model, based on automatically collected data, was developed for detection of subclinical mastitis. The logistic regression model was based on the following variables: milk electrical conductivity, milk production, parity, and DIM. Subclinical mastitis was defined as a minimal period of 1 wk in which the SCC was >500 × 103 cells/ml. In contrast, periods were defined as healthy if the SCC was <200 × l03 cells/ml.The resulting model had a sensitivity of 55% and specificity of 90% for individual milkings. For periods of 14 milkings, sensitivity was 54% and specificity 92% when the threshold for that period was >6 electrical conductivity signals for high SCC.Based on these test characteristics, the model could be used as an initial screening tool in a herd with a high incidence of subclinical mastitis. Cows with a signal would have a higher probability of being diseased than the total population. In such herds. separation of milk from the signaled cows might be a possible management strategy to reduce the SCC in the bulk milk tank.

Ibuprofen treatment of endotoxin-induced mastitis in cows

Summary Ibuprofen treatment was compared with saline solution treatment in an endotoxin-induced experimental model of bovine mastitis. Acute mastitis was induced in healthy lactating Holstein cows (n = 12) by intrammamary inoculation of 1 mg of Escherichia coli 026:B6 lipopolysaccharide in a single quarter per cow. Cows were assigned at random to ibuprofen (25 mg/kg of body weight, iv, n = 6) or 0.9% sodium chloride solution control (1.25 ml/kg, iv, n = 6) treatment groups. Ibuprofen or saline solution was administered once, 2 hours after endotoxin administration. The clinical course of endotoxin-induced mastitis and hematologic, clinical biochemical, and plasma mineral changes were monitored and compared between ibuprofen-treated and control cows. Clinical monitoring and blood sample collection were performed at 0, 2, 4, 6, 8, 12, 24, 48, 96, and 192 hours after endotoxin challenge. Rectal temperature and heart and respiratory rates were significantly (P ≤ 0.05) increased in saline treated cows, compared with cows treated with ibuprofen. Blood eosinophil count and serum phosphorus, sodium, and total carbon dioxide concentrations were significantly (P ≤ 0.05) decreased in saline-treated cows, compared with cows treated with ibuprofen. Ibuprofen treatment did not significantly change ruminations per minute, electrical conductivity of milk, quarter size, or quarter inflammation. The remaining hematologic, serum biochemical, plasma mineral, and coagulation values also were not changed significantly in response to ibuprofen treatment. Untoward effects attributed to ibuprofen administration were not observed. These results indicate that ibuprofen may provide empiric relief of clinical signs of coliform-induced mastitis.

The efficacy of in-line measurement of quarter milk electrical conductivity, milk yield and milk temperature for the detection of clinical and subclinical mastitis

The efficacy of in-line measurement of quarter milk electrical conductivity, milk yield and milk temperature for the detection of clinical and subclinical mastitis

The efficacy of in-line measurement of quarter milk electrical conductivity, milk yield and milk temperature for the detection of clinical and subclinical mastitis

In-line sensors were developed for measuring quarter milk electrical conductivity (QMC) to detect mastitis in dairy cattle. The sensors were built into the milking claws of a six-point open tandem milking parlour, in which 65 cows were milked daily. For individual cows, deviations were calculated between averages of the 20 highest values of QMC, measured every 5 s during milking. Over a 12-month period, 2-monthly bacteriological examinations, cell counts of quarter milk samples and observations for clinical mastitis served as references for the conductivity measurements. High and low thresholds for QMC were determined in order to detect severe (clinical) mastitis and subclinical mastitis, respectively. The first should be considered for treatment during lactation and the latter for dry cow therapy. The lowest running average QMC was defined as a “reference quarter” for each cow after each milking. A quarter indicated severe mastitis if its running average QMC deviated in excess of 15% from the reference quarter and if actual QMC was 20% over the running average of the reference quarter during two successive milkings. In considering dry cow therapy, cows were selected if the running average of QMC deviated≥10, 15 or 20% from the running average of the reference quarter during at least five milkings in the last 2 months of lactation. Automated data collection of daily milk yield and milk temperature in addition to QMC established the severity of mastitis. All cases of clinical mastitis (25) were detected by the high threshold values and 50% of the cases of subclinical mastitis and secretion disturbance were detected. Using the low threshold during the last 2 months of lactation, 50% of the cows were selected for dry cow therapy. In 24% of the cows recommended for treatment, there were no signs of subclinical mastitis in the quarter milk samples.

Precision conductometry in milk renneting

SummaryOn renneting, the electrical conductivity of milk decreased as viscosity increased. The sigmoidal time course of the decrease resembled the time course of shear modulus, but was more rapid. The total amount of change was independent of the amount of rennet and proportional to milk conductivity and its casein content. The conductivity change was interpreted as a change in the way casein micelles obstructed the path of the charge-carrying ions.

Possible role of sialic acid in bovine mastitis with particular reference to milk electrical conductivity.

SummaryMilk and serum sialic acid concentrations were measured in healthy and mastitic cows. In milk the mean sialic acid concentrations were 51 μ/ml in healthy cows, and 107 μ/ml in cows with mastitis. In serum the values were 517μ/ml and 698 μ/ml, respectively. Milk sialic acid was positively correlated with somatic cell counts (r = 0.48) and electrical conductivity (r = 0.53). Since sialic acids have electrolyte‐like properties and are also important regulators of tissue charges, their relation to electrical conductivity may be of special importance. Possible relations of sialic acid to milk somatic cells and milk coagulation are discussed.ZusammenfassungMögliche Bedeutung der Sialinsäure bei der bovinen Mastitis mit besonderer Bezugnahme auf die elektrische Leitfähigkeit der MilchBei gesunden und an Mastitis erkrankten Kühen wurden die Sialinsäure‐Konzentrationen in der Milch und im Serum gemessen. In der Milch gesunder Kühe lag die durchschnittliche Konzentration der Sialinsäure bei 51 μg/ml und in der Milch an Mastitis erkrankter Kühe bei 107 μg/ml. Im Serum betrugen die Werte 517 μg/ml bzw. 698 μg/ml. Milch‐Sialinsäure war positiv korreliert mit der Anzahl somatischer Zellen (r = 0,48) und der elektrischen Leitfähigkeit (r = 0,53). Da Sialinsäuren elektrolytartige Eigenschaften besitzen und ebenso wichtige Regulatoren der Gewebsladung darstellen, kann deren Beziehung zur elektrischen Leitfähigkeit von spezieller Bedeutung sein. Mögliche Beziehungen der Sialinsäure zu somatischen Zellen der Milch und zur Milchkoagulation werden diskutiert.

Pulsation failure as a consequence of milking with short teatcup liners

SummaryHalf-udder comparisons were made using 72 and 59 cows in 2 experiments of 8 months duration to assess the incidence of pulsation failure, and its effects on teat condition and mastitis, as a consequence of milking with shortened teatcup liners. Diagonally opposite quarters of all cows were milked with control liners of 148 mm effective length or with the same liners shortened to an effective length of 110–130 mm by inserting a rigid sleeve in the lower barrel of the liner. Milking with shortened teatcup liners resulted in a high incidence of pulsation failure (defined for this experiment as failure of the liner to collapse fully beneath the teat in each pulsation cycle throughout milking). More than 50% of hind teats and 90% of front teats in the research herd penetrated deeply enough into the teatcup to prevent cyclic collapse of liners shortened to 130 mm. Such failure occurred with all teats in liners shortened to 125 mm for front teats or 110 mm for hind teats. The combined results for the 2 experiments showed that almost twice as many new mastitis infections occurred in quarters milked with the shortened liners (47 υ. 25, P &lt; 0·01). This effect was confined to front quarters for which there was a 3-fold difference in the number of new infections (32 for the short liners υ. 11 for the controls, P &lt; 0·001). In both lactations, significantly more teats had petechial haemorrhages when milked with the shortened liners and the electrical conductivity of milk from uninfected front quarters was higher. These results might reflect an interaction between pulsation failure and differences in the degree of overmilking that usually occur between front and hind quarters.

Electrical conductivity of milk from goats of the Beetal breed.

Electrical conductivity of milk from goats of the Beetal breed.

The Age Thickening of Sweetened Condensed Milk III. Effect of Reaction and Changes in the Electrical Conductivity during Manufacture and Aging

In a sensitive colloidal system such as milk small changes in the .various ionic concentrations are likely to exert a great influence on the stability of the system. Since the electrical conductivity of milk depends upon the various ions present and the hindering effect of the non-electrolytes, it is reasonable to expect that any changes occurring in the ionic equilibrium and the concentration of the non-electrolytes during the manufacture and aging of sweetened condensed milk would be reflected in a change in the electrical conductivity. I t is a matter of common experience that an increase in .acidity almost invariably decreases the heat stability of milk. With these facts in mind, electrical condaetivity and hydrogen-ion concentration measurements were made during the preparation and aging of several batches of sweetened condensed milk. The different batches varied in their stability toward age thickening.