Detection of clinical mastitis by changes in electrical conductivity of foremilk before visible changes in milk

Genetic parameters for udder conformation traits derived from Cartesian coordinates generated by robotic milking systems in North American Holstein cattle

Udder conformation is directly related to milk yield, cow health, workability, and welfare. Automatic milking systems (AMS, also known as milking robots) have become popular worldwide, and the number of dairy farms adopting these systems have increased considerably over the past years. In each milking visit, AMS record the location of the 4 teats as Cartesian coordinates in a xyz plan, which can then be used to derive udder conformation traits. AMS generate a large amount of per milking visit data for individual cows, which contribute to an accurate assessment of important traits such as udder conformation without the addition of human classifier errors (in subjective scoring systems). Therefore, the primary objectives of this study were to estimate genomic-based genetic parameters for udder conformation traits derived from AMS records in North American Holstein cattle and to assess the genetic correlation between the derived traits for evaluating the feasibility of multi-trait genomic selection for breeding cows that are more suitable for milking in AMS. The Cartesian teat coordinates measured during each milking visit were collected by 36 milking robots in 4,480 Holstein cows from 2017 to 2021, resulting in 5,317,488 records. A total of 4,118 of these Holstein cows were also genotyped for 57,600 single nucleotide polymorphisms. Five udder conformation traits were derived: udder balance (UB, mm), udder depth (UD, mm), front teat distance (FTD, mm), rear teat distance (RTD, mm), and distance front-rear (DFR, mm). In addition, 2 traits directly related to cow productivity in the system were added to the study: daily milk yield (DY) and milk electroconductivity (EC; as an indicator of mastitis). Variance components and genetic parameters for UB, UD, FTD, RTD, DFR, DY, and EC were estimated based on repeatability animal models. The estimates of heritability (±standard error, SE) for UB, UD, FTD, RTD, DFR, DY, and EC were 0.41 ± 0.02, 0.79 ± 0.01, 0.53 ± 0.02, 0.40 ± 0.02, 0.65 ± 0.02, 0.20 ± 0.02, and 0.46 ± 0.02, respectively. The repeatability estimates (±SE) for UB, UD, FTD, RTD, and DFR were 0.82 ± 0.01, 0.93 ± 0.01, 0.87 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. The strongest genetic correlations were observed between the FTD and RTD (0.54 ± 0.03), UD and DFR (-0.47 ± 0.03), DFR and FTD (0.32 ± 0.03), and UD and FTD (-0.31 ± 0.03). These results suggest that udder conformation traits derived from Cartesian coordinates from AMS are moderately to highly heritable. Furthermore, the moderate genetic correlations between these traits should be considered when developing selection sub-indexes. The most relevant genetic correlations between traits related to cow milk productivity and udder conformation traits were between UD and EC (-0.25 ± 0.03) and between DFR and DY (0.30 ± 0.04), in which both genetic correlations are favorable. These findings will contribute to the design of genomic selection schemes for improving udder conformation in North American Holstein cattle, especially in precision dairy farms.

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

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

Relationship among Milk Conductivity, Production Traits, and Somatic Cell Score in the Italian Mediterranean Buffalo

The measurement of milk electrical conductivity (EC) is a relatively simple and inexpensive technique that has been evaluated as a routine method for the diagnosis of mastitis in dairy farms. The aim of this study was to obtain further knowledge on relationships between EC, production traits and somatic cell count (SCC) in Italian Mediterranean Buffalo. The original dataset included 5411 records collected from 808 buffalo cows. Two mixed models were used to evaluate both the effect of EC on MY, PP and FP and EC at test-day, and the effect of EC on somatic cell score (SCS) by using five different parameters (EC_param), namely: EC collected at the official milk recording test day (EC_day0), EC collected 3 days before official milk recording (EC_day3), and three statistics calculated from EC collected 1, 3 and 5 days before each test-day, respectively. All effects included in the model were significant for all traits, with the only exception of the effect of EC nested within parity for FP. The relationship between EC and SCS was always positive, but of different magnitude according to the parity. The regression of EC on SCS at test-day using different EC parameters was always significant except when the regression parameter was the slope obtained from a linear regression of EC collected over the 5-day period. Moreover, in order to evaluate how well the different models fit the data, three parameters were used: the Average Information Criteria (AIC), the marginal R2 and the conditional R2. According to AIC and to both the Marginal and Conditional R2, the best results were obtained when the regression parameter was the mean EC estimated over the 5-day period.

Use of milk electrical conductivity for the differentiation of mastitis causing pathogens in Holstein cows

Mastitis is one of the most prevalent and costly diseases in dairy cattle. Key components for adequate mastitis control are the detection of early stages of infection, as well as the selection of appropriate management interventions and therapies based on the causal pathogens associated with the infection. The objective was to characterize the pattern of electrical conductivity (EC) in milk during intramammary infection, considering specific mastitis-causing pathogen groups involvement. Cows (n = 200) identified by an in-line mastitis detection system with a positive deviation ≥15% in the manufacturer's proprietary algorithm for EC (high electrical conductivity (HEC)) were considered cases and enrolled in the study at the subsequent milking. One control (CON) cow, within normal ranges for EC, was matched to each case. A composite milk sample was collected aseptically from each cow for bacteriological culture. Milk yield (MY) and EC were recorded for each milking during ±7 days relative to enrollment. Milk cultures were categorized into gram positive (GP), gram negative (GN), other (OTH) and no growth (NOG). Data were submitted for repeated-measures analysis with EC as the dependent variable and EC status at day -1, bacteriological culture category, parity number, stage of lactation and days relative to sampling as main independent variables. Average (± standard error (SE)) EC was greater in HEC than in CON cows (12.5 ± 0.5 v. 10.8 ± 0.5 mS/cm) on the day of identification (day -1). Milk yield on day -1 was greater in CON than in HEC (37.6 ± 5.1 v. 33.5 ± 5.2 kg). For practical management purposes, average EC on day -1 was similar for the different bacteriological culture categories: 11.4 ± 0.6, 11.7 ± 0.5, 12.3 ± 0.8 and 11.7 ± 0.5 mS/cm in GN, GP, OTH and NOG, respectively. Parity number was only associated with day -1 EC in HEC group, with the greatest EC values in parity 3 (12.3 ± 0.3 mS/cm), followed by parity 2 (11.9 ± 0.2 mS/cm), parity >3 (11.6 ± 0.5 mS/cm) and primiparous cows (11.2 ± 0.2 mS/cm). An effect on EC for the interaction of day relative to identification by pathogen gram category was observed. The same interaction effect was observed on daily MY. Overall, the level of variation for MY and EC between- and within-cows was substantial, and as indicated by the model diagnostic procedures, the magnitude of the variance in the cows in the CON group resulted in deviations from normality in the residuals. We concluded that characteristic temporal patterns in EC and MY in particular pathogen groups may provide indications for differentiation of groups of mastitis-causing pathogens. Further research to build detection models including EC, MY and cow-level factors is required for accurate differentiation.

Reducing milking frequency in early lactation improved the energy status but reduced milk yield during the whole lactation of primiparous Holstein cows consuming a total mixed ration and pasture

To investigate the immediate and long-term performance effects of milking frequency during early lactation of primiparous dairy cows consuming a total mixed ration and pasture, 20 Holstein cows were assigned in a randomized block design to either once-daily (1×) or twice-daily (2×) milking during the first 8 wk of lactation (treatment period). After the treatment period, all cows were milked 2× until wk 43 of lactation. Cows were fed a total mixed ration (approximately 15 kg of DM/cow per day) and allowed to graze an oat pasture (Avena sativa). Dry matter intake was 19.1 kg of DM/cow per day on average and was not affected by treatments. Milk yield was 40% lower in cows milked 1× during the treatment period, and a carryover effect existed until wk 21 of lactation, resulting in a final reduction of 15% of milk yield in the whole lactation. Milk lactose concentration decreased, whereas fat and protein concentrations increased for cows milked 1×. Mobilization of energy reserves during the treatment period occurred in both groups, but cows milked 1× showed greater body condition score and greater backfat thickness. In conclusion, milking 1× during the first 8 wk of lactation resulted in immediate and carryover negative effects on milk and milk solid yield without affecting feed intake, resulting in the improved energy status of primiparous dairy cows.

Immunodetection of Streptococcus uberis pathogen in raw milk

Streptococcus uberis is a major mastitis-causing environmental pathogen, which rapid immunodetection has not been possible due to the absence of specific anti-Str. uberis antibodies. Recently, a specific antibody against the Str. uberis adhesion molecule (SUAM) has been designed. In the present study, the specificity and affinity of this antibody towards SUAM antigenic region SAPVYLGVSTE and Str. uberis cells are characterized, using experimental and in silico bioinformatic methods. The selectivity studies and bioinformatic analyses revealed high specificity of the antibody towards Str. uberis. The K_d value of SAPVYLGVSTE/anti-Str. uberis antibody complex was 27 ± 6 nM, indicating the applicability of this antibody for the detection of Str. uberis. The anti-Str. uberis antibody was used as a specific biorecognition element of a biosensor for the detection of Str. uberis bacteria in phosphate buffer and in milk and these analyses took less than 20 min. The Str. uberis biosensor was also tested in the milk of cows suffering from mastitis and the obtained results were in good agreement with the conventional identification of this pathogen by microbiological plating.

Quarter-milking-, quarter-, udder- and lactation-level risk factors and indicators for clinical mastitis during lactation in pasture-fed dairy cows managed in an automatic milking system

OBJECTIVES

  To identify risk factors and indicators at quarter-milking and other levels for clinical mastitis during lactation in pasture-fed cows managed in an automatic milking system.

DESIGN

  A retrospective nested case-control study using data collected over a 31-month period. A total of 803,948 quarter-milkings from 249 cows were available for analysis, of which 245 were selected as clinical mastitis quarter-milking cases and 2450 unaffected quarter-milkings were selected as controls. Most of the diagnoses of clinical mastitis had been made after clinical assessments of quarters following automated alerts by the milking system. Potential risk factors and indicators were assessed using univariable and multivariable multilevel logistic models.

RESULTS

  Quarters that are milked infrequently and have low yield, fast peak milk flow rates, blood in the milk and/or elevated milk conductivity are at increased risk of clinical mastitis. Quarters were also at increased risk between days 10 and 29 of lactation, and during higher parity lactations. Milk fat and protein concentrations and the fat:protein ratio were not significantly associated with the development of clinical mastitis. Neither quarter position (fore or rear) nor side (left or right) was associated with clinical mastitis occurrence. Quarters that had been clinically affected more than 30 days previous in the current lactation were not at increased risk of redeveloping clinical mastitis.

CONCLUSIONS

  Under the study conditions, the risk of clinical mastitis increased for quarter-milkings in quarters that were milked less frequently in the previous 7 days, were low-yielding, had fast peak milk flow rates and had more milkings in which blood was detected in milk and/or the milk conductivity was elevated. Risk was also increased in days 10-29 of lactation and in higher parity lactations.

Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks

Two types of artificial neural networks, multilayer perceptron (MLP) and self-organizing feature map (SOM) were used to detect mastitis by automatic milking systems (AMS) using a new mastitis indicator that combined two previously reported indicators based on higher electrical conductivity (EC) and lower quarter yield (QY). Four MLPs with four combinations of inputs were developed to detect infected quarters. One input combination involved principal components (PC) adopted for addressing multi-collinearity in the data. The PC-based MLP model was superior to other non-PC-based models in terms of less complexity and higher predictive accuracy. The overall correct classification rate (CCR), sensitivity and specificity of this model were 90·74%, 86·90% and 91·36%, respectively. The SOM detected the stage of progression of mastitis in a quarter within the mastitis spectrum and revealed that quarters form three clusters: healthy, moderately ill and severely ill. The clusters were validated using k-means clustering, ANOVA and least significant difference. Clusters reflected the characteristics of healthy and subclinical and clinical mastitis, respectively. We conclude that the PC based model based on EC and QY can be used in AMS to detect mastitis with high accuracy and that the SOM model can be used to monitor the health status of the herd for early intervention and possible treatment.

Mastitis detection: current trends and future perspectives

Bovine mastitis, the most significant disease of dairy herds, has huge effects on farm economics due to reduction in milk production and treatment costs. Traditionally, methods of detection have included estimation of somatic cell counts, an indication of inflammation, measurement of biomarkers associated with the onset of the disease (e.g. the enzymes N-acetyl-beta-D-glucosaminidase and lactate dehydrogenase) and identification of the causative microorganisms, which often involves culturing methods. These methods have their limitations and there is a need for new rapid, sensitive and reliable assays. Recently, significant advances in the identification of nucleic acid markers and other novel biomarkers and the development of sensor-based platforms have taken place. These novel strategies have shown promise, and their advantages over the conventional tests are discussed.

Production of hydrogen peroxide by a small molecular mass compound in milk from Holstein cows with high and low milk somatic cell count

Mastitis is the most frequent and prevalent production disease in dairy herds in developed countries. Based on a milk somatic cell count (SCC) of either >300,000 or <200,000 cells/ml in this study, we defined the quarter as either inflamed or uninflamed, respectively. The electrical conductivity (EC) of milk was used as an indicator of udder epithelial cell damage. We determined the amount of H2O2 produced by utilizing a small molecular weight compound in milk, and examined the characteristics of H2O2 production and EC in milk from inflamed and uninflamed quarters. In cows with milk of delivery grade (control population), H2O2 production and EC were 3.6+/-1.3 nmol/ml and 5.4+/-0.4 mS/cm (mean+/-sd), respectively. In 37 inflamed quarter milk samples, the production of H2O2 was 1.9+/-1.0 nmol/ml and was significantly smaller than that in the control population (P<0.01). Production of H2O2 was moderately but significantly correlated with EC (r<-0.71). In 20 cows with inflamed quarters, the production of H2O2 in milk from inflamed quarters was significantly smaller than that in milk from uninflamed quarters (P<0.01). In 18 out of 20 cows, milk from inflamed quarters showed the smallest H2O2 production among all tested quarters in each cow. We conclude that inflammation caused a decrease in H2O2 production in milk. In this study, we present parameters for evaluating the lactoperoxidase/H2O2/thiocyanate antibacterial defence system in bovine milk.

The use of a hand-held conductivity meter for the diagnosis of subclinical mastitis in dairy cows during late lactation

AIM

To evaluate the accuracy of a hand-held electrical conductivity meter for the detection of subclinical mastitis in an Australian dairy herd in late lactation.

METHODS

A hand-held conductivity meter was evaluated during late lactation in a herd of dairy cows that had a high prevalence of Streptococcus agalactiae and Staphylococcus aureus infection. The ability of the conductivity meter to accurately discriminate between uninfected quarters and those infected with major pathogens was assessed, using bacteriology as the definitive test for infection status. Milk samples for bacteriology, and electrical conductivity measurements, were collected from 233 quarters from 59 cows. The ability of the device to identify infected and uninfected cows was also assessed. Sensitivity and specificity were calculated for a range of threshold values, using absolute values, the range of quarter values within a cow and the ratio of quarter values within a cow.

RESULTS

Electrical conductivity was higher in infected quarters than uninfected quarters, but the degree of overlap between the populations was substantial, even when quarters were assessed as case-control pairs from the same cow. Use of the device according to the manufacturer's recommendations for diagnostic criteria of mastitic quarters had a sensitivity of 51% and a specificity of 71%. The ability of the device to correctly diagnose the infection status of cows was dependent on both the analytical method and the threshold value used. Applying the manufacturer's recommended criteria to the diagnosis of the status of cows, the sensitivity of the test was 91% and the specificity 17%.

CONCLUSION

The variability between individual quarters and between cows in this study was such that the use of this device to measure the conductivity of milk was not a reliable method by which to diagnose subclinical mastitis in cows in late lactation.

CLINICAL RELEVANCE

Interpretation of results from handheld conductivity meters when used for the diagnosis of subclinical mastitis in dairy cows needs to be made with care. In some circumstances the results obtained do not accurately reflect the bacteriological status of either individual quarters, or cows.

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.

Investigation of mastitis problems on farms

The production of high-quality milk is a high-priority issue for dairy farmers and milk processors. The investigation of mastitis problems on dairy farms is an area of increasing demand and is an ideal way for veterinary practitioners to increase involvement in production medicine programs. Goals for the amount of clinical and subclinical mastitis should be predefined on dairy farms, and a herd investigation initiated when needed. The use of a structured approach to mastitis problems can identify risk factors for infection, result in rapid resolution of mastitis problems, and hasten the application of appropriate preventive practices.

Simultaneous intramammary and intranasal inoculation of lactating cows with bovine herpesvirus 4 induce subclinical mastitis

In this study, we examined whether an experimental bovine herpesvirus 4 (BHV4) infection can induce bovine mastitis, or can enhance bovine mastitis induced by Streptococcus uberis (S. uberis). Four lactating cows were inoculated intramammarily and intranasally with BHV4, and four lactating control cows were mock-inoculated. After 14 days, two of four cows from each group were inoculated intramammarily with S. uberis. No clinical signs were recorded in cows inoculated only with BHV4, and their milk samples showed no abnormal morphology, despite the fact that BHV4 replicated in inoculated quarters. Somatic cell count increased significantly in milk from three of six BHV4-inoculated quarters, compared to the non-inoculated quarters of the same cows (within-cow) and the quarters of mock-inoculated cows (control group) on days 8, 9 and 11 post-inoculation (pi). BHV4 was isolated from nasal swabs between days 2 and 9 pi. Clinical mastitis was observed in all four cows intramammarily inoculated with S. uberis. A preceding BHV4 infection did not exacerbate the clinical mastitis induced by S. uberis. S. uberis infections appeared to trigger BHV4 replication. From one quarter of each of two cows inoculated with BHV4 and S. uberis, BHV4 was isolated, and not from quarters inoculated with BHV4 only. In conclusion, BHV4 did not induce bovine clinical mastitis after simultaneous intranasal and intramammary inoculation. However, the BHV4 infection did induce subclinical mastitis in 50% of the cows and the quarters.

Application of the California mastitis test in intramammary Streptococcus agalactiae and Staphylococcus aureus infections of camels (Camelus dromedarius) in Kenya

A study was conducted on 207 lactating camels in six herds in Kenya to evaluate the California mastitis test (CMT) for the detection of intramammary infections (IMIs) caused by Streptococcus agalactiae and Staphylococcus aureus and to investigate the prevalence of both the pathogens in the camel udder. IMI with S. agalactiae was found in 12% of all camels sampled. IMI with S. aureus was present in 11% of all camels sampled. The herd-level prevalence of IMI varied between 0 and 50% for S. agalactiae and between 0 and 13% for S. aureus. Longitudinal observations over 10-12 months confirmed persistent infections for both pathogens. Observations in one herd suggested that camel pox was a contributing factor in spreading and exacerbating S. agalactiae udder infections.The CMT had quarter-level sensitivities of 77 and 68% for S. agalactiae and S. aureus in camels, respectively. The CMT specificities were 91% for both the pathogens.

Vaccines and diagnostic methods for bovine mastitis: fact and fiction

A number of problems are uniquely associated with vaccination of dairy cows for mastitis. One of these is that the number of mastitis pathogens is numerous and heterogeneous. Vaccine efforts have concentrated mainly on the major mastitis pathogens. While at least one S. aureus bacterin has been commercially available for a number of years, no large-scale, independent field trials have been published in refereed journals which support the efficacy of this vaccine. Experimental vaccines for S. aureus composed of pseudocapsule-enriched bacterins supplemented with alpha- and/or beta-toxoids appear promising, but none of these has been commercialized. With S. uberis, some protection against homologous strain challenges was reported recently with a live strain and a bacterin, but other data from the same laboratory showed this vaccine would not protect against heterologous challenge strains. At this time there is only one highly effective vaccine for mastitis, the core-antigen vaccine for coliform mastitis. All of the commercially available vaccines for this indication are bacterins of rough mutants of E. coli strain J5 or Salmonella spp. Preliminary success with an experimental vaccine based on the plasminogen activator of S. uberis is a very different approach for a mastitis vaccine. Little success has been reported with vaccination against other mastitis pathogens. For diagnostic methods, the high somatic cell count, as measured by direct count or indirect assays, remains the cornerstone of mastitis diagnosis. However, for subclinical mastitis, bacterial cell culture is a reliable diagnostic method. Pathogen identification may rely on older biochemical testing methods or newer commercial identification systems, depending on the laboratory budget. ELISA assays also have been used to assess herd infection status. Epidemiologic studies have used DNA fingerprinting and ribotyping, but none of these methods has yet produced an easily utilized commercial format. Within the next decade, additional efficacious vaccines for several of the most common agents for bovine mastitis are likely. A review written at that time then can be more fact than fiction.

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 x 10(6) cells/ml for both pathogens, which was significantly less than when clots appeared at conventional diagnosis, approximately 4 x 10(6) and 12 x 10(6) cells/ml for Staph. aureus and Strep. uberis, respectively. The time required for SCC to recover to < 4 x 10(5) 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 < or = 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.