Milk Electrical Conductivity Research Articles

Effects of subclinical mastitis on automatic milking system data, hematological and biochemical parameters, and milk composition in Holstein cows.

Subclinical mastitis decreases milk production and quality, despite the normal appearance of the mammary glands and milk. Herein, we aimed to investigate changes in factors monitored via automatic milking systems (AMS) prior to subclinical mastitis onset and identify differences in hematological and biochemical parameters and milk composition at subclinical mastitis onset. Thirty-two Holstein cows were divided into two groups according to somatic cell counts (SCC) from AMS and milk composition analysis and the California mastitis test (CMT): healthy cows (controls [CON], n=16, SCC <500×103 cells/ml and negative for CMT) and cows with subclinical mastitis (SCM, n=16, SCC ≥500×103 cells/ml and positive for CMT). Eventually, 121 milk samples from the CON ([mCON], n=60) and SCM ([mSCM], n=61) groups were obtained; SCM samples were categorized as those from non-inflamed (mNQ) or subclinically-inflamed (mIQ) quarters. We evaluated AMS factors; hematological, biochemical, and milk composition parameters; and bacterial isolation. In cows with SCM, milk yield decreased and electrical conductivity (EC) changed before disease onset. Milk EC decreased in mNQ although increased in mIQ (p<0.05). The SCM group had higher globulin levels and lower basophil counts; albumin-to-globulin ratio; and total cholesterol, albumin, and blood urea nitrogen levels than the CON group (p<0.05). The mIQ group had higher SCC but lower levels of lactose and milk solids-not-fat than those in the mCON and mNQ groups (p<0.05). The mCON group had higher levels of milk non-protein nitrogen than the mNQ group (p<0.05). Opportunistic mastitis pathogens were isolated in the mIQ group. Changes in milk yield and EC measured using AMS occurred prior to subclinical mastitis, which may be associated with variation in basophil counts; albumin-to-globulin ratio; and total cholesterol, albumin, BUN, globulin, SCC, milk lactose, and milk solids-not-fat levels at disease onset. These findings provide new insights into early-stage subclinical mastitis.

Association of Dry Period Length with Automatic Milking System, Mastitis, and Reproductive Indicators in Cows.

The objective of this study was to evaluate the association between dry period (DP) length and various indicators of productivity, reproduction, and udder health in cows managed with an automatic milking system. We analyzed records from 3861 cows, categorizing them into three groups based on their DP duration: (1) <40 days, (2) 40-70 days, and (3) DP > 70 days. Cows with a DP of 40-70 days had an average energy-corrected milk production that was 8.2 kg greater than that of cows with a short DP and 5.0 kg greater than that of cows with a long DP (p < 0.001). Milk from the 40-70-day DP group exhibited the highest lactose concentration (4.64 ± 0.01%). Additionally, cows with the longest DP had the smallest proportion of animals with a milk fat-to-protein ratio of 1.2 to 1.4. Cows with a DP of 40-70 days also showed the lowest milk electrical conductivity across all udder quarters, whereas cows with the shortest DP had the highest conductivity. The highest conception rates were observed in the group with the shortest DP. These results suggest that a DP of 40-70 days is optimal for maximizing milk production and improving both udder health and reproductive performance under AMS. Proper management of DP duration can be an effective strategy for sustainable dairy herd management.

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 &lt; 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.

Welfare assessment traits, milk quantity and quality, and profitability of Anatolian buffalo cows confined in closed-tied or semi-open free-stall barns can be affected by supplementary feeding at milking.

This study was conducted to evaluate the effect of offering (OSF) or not (NSF) supplemental feed at milking on temperament (TS), udder hygiene (UHS) and body condition (BCS) scores, and milk yield per milking (MYM), milk quality traits, and profitability of primiparous Anatolian buffalo cows at 90 days of lactation confined in closed-tied (CB) or semi-open free-stall (OB) barns. In Experiment I, 108 cows were selected to encompass four treatments (OBOSF, OB-NSF, CB-OSF, and CB-NSF) of 27 cows, considering barn type (OB and CB) and supplementary feed (OSF and NSF) at milking. In Experiment II, 60 OB cows were selected to encompass one of five groups of 12 cows each: i) no supplemental feed (CON), ii) commercial concentrate (CC), iii) CC + corn silage (CCS), iv) CCS + alfalfa hay (CSA), or v) CC + ryegrass silage (CRS) at milking. The TS and UHS of the OB and OSF cows were lower (better) than those of the CB and NSF cows, respectively. The OSF increased milk protein, lactose, and solids-not-fat but decreased milk freezing point and electrical conductivity compared with the NSF. The MYM and milk fat of the OB-OSF cows were higher than those of the OB-NSF and CBNSF cows. The TS and UHS of the cows negatively correlated with MYM and some milk chemicals (fat, protein, and solids-not-fat), but BCS correlated positively. The TS and milk electrical conductivity of the CCS, CSA, and CRS cows were lower than those of the CON and CC cows, but BCS, MYM, and milk fat were higher. Partial budget analysis identified a higher net profit for supplemental feed-offered groups (OB-OSF, CCS, CSA, and CRS). Offering roughage with concentrates at milking for indoor primiparous buffalo cows is more conducive to well-being, milk yield, milk quality, and economy.

Indirect Sensing of Subclinical Intramammary Infections in Dairy Herds with a Milking Robot.

This study determined the impact of subclinical intramammary infections (IMIs), such as the major and minor udder pathogens (MaPs and MiPs), on the somatic cell count (SCC) in cow milk and investigated the possibilities of indirect sensing of the udder pathogens using the mastitis detection index (MDi) (DeLaval, Tumba, Sweden). The MDi incorporates quarter-level milk electrical conductivity, blood in milk, and milking interval. The case group (n = 21; MDi ≥ 1.4) was compared with the control group (n = 24; MDi < 1.4) for the presence of IMIs. The microbiological investigation of udder quarter foremilk samples was performed two times with an interval of 10 to 14 days. The case and control groups differed in terms of the occurrence of MaPs and MiPs in milk. During the continuous subclinical IMI and the episodic MaP infection, a higher SCC was detected compared with the episodic MiP infection or quarters without IMI. The novel finding of this study was that by using the milk quality sensor for the sensing of subclinical IMIs, there was an indication for the successful detection of episodic MaPs. However, the sensing of the continuous subclinical IMIs was not possible in the current study and still needs to be investigated.

Effects of Sexed and Conventional Semen Use and Calf Sex on Milk Yield Parameters, Body Weight and Milk Electrical Conductivity in Holstein Cows

This study was designed to determine the effects of semen type and calf gender on milk yield parameters, milk electrical conductivity and milk flow values and postpartum maternal body weight. A total of 265 Holstein cows in the first lactation were used in the study. The effect of sperm type on maternal body weight levels after calving and milk electrical conductivity values was significant (P0.05). Heifers inseminated with sexed semen had higher 100-days milk yield, milk peak yield and postpartum maternal body weight in the first lactation. Milk peak duration was higher in heifers inseminated with conventional semen (P0.05). In conclusion, heifers inseminated with sexed semen had higher maternal body weights, 100-days milk yield, milk peak yields and milk electrical conductivity levels in the first lactation than inseminated with conventional semen

Effect of the Onset of Intramammary Infection on the Electrical Conductivity of Ewe's Milk and Study of Various Algorithms for Its On-Line Detection.

The aim of this study was to determine the effect of the onset of intramammary infection (IMI) on the electrical conductivity (EC) of ewe milk and assess the detection capability of various algorithms based on daily glandular milk EC measurement. An experiment was carried out with 26 Manchega sheep located at the farm of the Miguel Hernández University, Elche, Spain. The variables in milk from the gland (production, EC) were monitored daily for 2 weeks during the morning and evening milking; once infection was established in the gland, the variables were measured for a further 4 weeks. In addition, the SCC, sodium, potassium, chloride and milk macro-compositions were analysed. The sensitivity, specificity and positive and negative predictive values for IMI detection of different algorithms were calculated using the EC variable. It was observed that the onset of IMI resulted in an increase in SCC and a significant decrease in yield, and EC rose significantly when infection occurred bilaterally. The best results for IMI detection were obtained with the algorithm that detected deviations greater than 3σ of the conductivity ratio between collateral glands with respect to a moving average calculated with a time horizon of 10 days (50% sensitivity and 100% specificity).

Sensor-Generated Data for Evaluation of Subclinical Mastitis Treatment Effectiveness with Garlic Extract (Allicin) in Dairy Cattle

The aim of this study was to determine the impact of subclinical mastitis treatment in dairy cattle on biomarkers registered with in-line sensors such as milk yield (MY), electric milk conductivity (EC), rumination time (RT), and somatic cell count (SCC). At the start of the experiment, all cows according to SCC level were divided into two groups: healthy cows (n = 30, with SCCs less than 200,000 per mL and without the growth of bacteria in the milk samples) and cows with subclinical mastitis (n = 32), with SCC levels greater than 200,000 per mL and with growth of bacteria. Streptococcus spp. was found in 15 samples, and Strep. uberis was found in 17 samples. Streptococcus spp. and Strep. uberis were sensitive to amoxicillin and calvulanic acid. According to these results, 32 cows with subclinical mastitis were treated with two treatment protocols: one 1 (n = 16) and two (n = 16). In the first protocol, we used SCC boluses and nonsteroidal anti-inflammatory drugs (SCCB and NSAID). The second protocol consists of intramammary antibiotics and anti-inflammatory medications (Synulox LC and NSAIDs). All parameters (MY, EC, RT, and SCC) were recorded with Lely Astronaut® A3 milking robots on the day of mastitis diagnosis (0 day) and 14 days after treatment began. All animal experimental procedures were approved by the ethical committee; the approval number is PK01696. On the basis of our findings, we may infer that SCC boluses and NSAIDs are effective in treating subclinical mastitis. After 14 days of treatment, the electrical conductivity of milk in cows treated with AB and NSAID was also higher in all quarters of the udder compared to cows treated with SCCB + NSAID. The RT of cows on disease diagnosis day of cows treated with AB and NSAID was 11.41% lower compared to cows treated with SCCB and NSAID, while the RT of cows after 14 days treated with AB and NSAID was 7.01% lower compared to cows treated with SCCB and NSAID. On the practical side, for treatment of subclinical mastitis, we recommend using a feed supplement SCC bolus (one per os) with a composition containing Meloxicam 20 mg with a single subcutaneous injection at a dosage of 2.5 mL per 100 kg body weight.

Genomic-based genetic parameters for milkability traits derived from automatic milking systems in North American Holstein cattle

The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from -0.48 to -0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (-0.58 ± 0.02) and MFAIL and FRM (-0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.

Association between Milk Electrical Conductivity Biomarkers with Lameness in Dairy Cows.

Early identification of lameness at all phases of lactation improves milk yield and reduces the incidence of mastitis in the herd. According to the literature we hypothesized that there are associations of electrical conductivity variables of milk flow with lameness in dairy cows. The aim of this study was to determine if blood cortisol and electrical conductivity in the milk flow phases correlate with each other and whether they are related to cow lameness. On one farm, out of 1500 cows, 64 cows with signs of lameness and 56 healthy cows were selected with an average of 2.8 lactations and 60 days in the postpartum period. A local veterinarian who specializes in hoof care treatments identified and scored lameness. During evening milking, the milk flow of all 120 cows was measured using electronic milk flow meters (Lactocorder®, WMB AG, Balgache, Switzerland). Before each milking, two electronic mobile milk flow meters (Lactocorders) were mounted between the milking apparatus and the milking tube to take measurements. We found that the average cortisol concentration in the blood of the studied cows was significantly correlated with the laminitis score. Results of this study indicate that the number of non-lame cows with a milk electrical conductivity level of &lt;6 mS/cm even reached 90.8-92.3% of animals. Milk electrical conductivity indicators ≥ 6 mS/cm were determined in 17.8-29.0% more animals in the group of lame cows compared to the group of non-lame cows. According to our study, we detected that blood cortisol concentration had the strongest positive correlation with milk electrical conductivity indicators. Cows with a greater lameness score had a higher cortisol content and milk conductivity.

Genetic associations between mastitis, milk electrical conductivity, and milk flow rate in temperate dairy cows in tropics

Milk electrical conductivity and milk flow rate are potential indicators in selection targeting mastitis resistance. This study aimed to estimate the genetic parameters for clinical mastitis, average daily milk electrical conductivity (EC), milk flow rate (FR), and milk yield (MY) for the first lactation of imported Jersey and Jersey-Friesian dairy cows in Sri Lanka. Genetic parameters for mastitis traits, the prevalence of clinical mastitis in each cow, the duration of clinical mastitis treatment, and the number of episodes of mastitis were estimated using univariate sire models with permanent environmental effect of the cow added to models for prevalence of clinical mastitis in each day of milk over the whole lactation, or within the first 100-days of milk. The genetic parameters for the traits EC, FR, and MY over the whole lactation and for these traits defined for 30-day intervals were estimated using univariate animal models. For all mastitis traits, a zero heritability was estimated under both normal and logit scales. Heritabilities of EC and FR ranged from 0.02 to 0.11 and from 0.02 to 0.14, respectively. Genetic correlations between MY and EC and between MY and FR around peak milk yield were 0.25 ± 0.46 and 0.46 ± 0.29, respectively. Based on the heritability estimates for mastitis, dairy cows in this study cannot be directly selected for mastitis resistance, but indirect selection via EC and FR is feasible. Recording all cases of both sub-clinical and clinical mastitis in subsequent lactations is recommended to aid selection of dairy cows for mastitis resistance.

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

Simple SummaryMastitis can be considered one of the costliest diseases of the dairy Mediterranean Buffalo. Early detection of the disease is thus of great importance to farmers, to reduce or prevent production losses. The measurement of electrical conductivity (EC) of milk is a relatively simple and inexpensive technique, and it has been studied as a routine method for the diagnosis of mastitis in dairy farm. Limited information is currently available on the relationships among EC, production traits and somatic cells count in Italian Mediterranean Buffalo. Hence, the aim of this study was to investigate those correlations using data collected at a commercial Italian buffalo farm. We can conclude that in buffalo, as in other species, there is a strong relationship between EC and somatic cells. Furthermore, we observed that, if the objective is to have an additional and informative parameter for early detection of mastitis, frequent EC recording, possibly performed over a longer period of time, is more effective than recording EC only a few times. Even if our results are encouraging, further studies are needed, in order to validate them, especially if the objective is the development of udder disease prediction models.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.

The Indicators of Clinical and Subclinical Mastitis in Equine Milk.

Simple SummaryMastitis, the inflammation of the mammary gland, is a major problem in a mare’s perinatal period, negatively affecting both the health of the mare and newborn foal and the quality of milk produced on dairy equine farms. The detection of mastitis is therefore one of the important goals in the equine breeding and dairy industry. This study aimed to determine the somatic cell count (SCC), the percentage of the immune cells, the electrical conductivity (ECM), and bacteriological index (BII) in milk collected from mares with (CM) and without (NCM) clinical symptoms of mastitis. The increase in examined indicators is suspected to be a subclinical mastitis indicator, therefore the study aimed to separate two subgroups, mares with (SM) and without (NSM) subclinical symptoms of mastitis. In milk from NCM mares the values of SCC, immune cells, and ECM increased immediately after birth and weaning, whereas during the rest of the lactation period their values were at a low level. Similarly, in milk from CM mares, the values of examined indicators were high and comparable to the weaning time. An increase in the level of examined indicators may become an early indication of subclinical mastitis.The somatic cell count in milk (SCC) and electrical conductivity of milk (ECM) are indicators of the health status of the mammary gland. Among somatic cells, mainly polymorphonuclear neutrophils (PMN), macrophages (MAC), and lymphocytes (LYM) are rated. This study aimed to determine the SCC, PMN, MAC, LYM, ECM, and bacteriological index (BII) in milk collected from mares with (CM) and without (NCM) clinical symptoms of mastitis concerning mares with (SM) and without (NSM) subclinical mastitis. Milk samples were collected from 27 mares divided into NCM (n = 12) and CM (n = 15) groups. In samples, SCC quantification, cytological examinations, ECM measurement, and bacteriological examination were performed. In NCM mares, the values of SCC, PMN, MAC, LYM, and ECM were higher in initial than in consecutive examined days after birth until weaning. After weaning the proportion of SCC, PMN, MAC, LYM, ECM, and BII increased and did not differ with the average values in CM mares. These equine milk indicators may reflect an early symptom of subclinical mastitis and in the future may be used in the early detection of mastitis or as a tool of assessment of the health status of the mammary gland in the dairy equine farm.

The Effects of Replacing Soybean Meal with Rapeseed Meal, Cottonseed Cake, and Fava Beans on the Milk Yield and Quality Traits in Milking Ewes.

Simple SummaryThe substitution of soybean meal in farm animal diets is considered vital for the economic and environmental sustainability of the livestock sector. However, data regarding the effects of a soybean meal replacement on the milk yield and quality traits in dairy sheep are scarce. In our study, two isonitrogenous and isoenergetic diets were used, with soybean meal of a typical ration being replaced by a mixture of rapeseed meal, cottonseed cake, and fava beans. The milk yield and the body condition scores were recorded, and milk samples were analyzed monthly for their fat, protein, lactose, and total solids yields, as well as for somatic cell counts, total bacterial counts, pH, electrical conductivity, and the refractive index. Daily and 100-day fat yields were significantly increased in the group fed the experimental ration and the electrical conductivity was significantly decreased in the same group, while no adverse effects on any of the rest of the studied milk production traits were observed.The replacement of soybean meal (SBM) from intensively reared dairy sheep diets has emerged as a significant challenge for sustainable production. However, the effects of this replacement on milk production have not been sufficiently elucidated. The objective of this study was to prospectively assess the effects of replacing SBM with a mixture of alternative protein sources on the milk yield (MY) and the milk quality traits (MQT) in intensively reared dairy sheep. A total of 112 multiparous, purebred milking ewes of the Chios and Frizarta breeds, from two intensive dairy sheep farms, were involved in the study, postweaning, and were assigned to either the control (CR) or the experimental ration (ER) group. In the ER, 3/4 of the SBM was replaced by a mixture of rapeseed meal, cottonseed cake, and fava beans, producing a ration of a similar nutritional value. MY, MQT, and body condition scores were recorded for each individual ewe monthly for a period of 4 months during lactation. The experimental ration was associated with beneficial effects on daily and 100-day fat yields and on the electrical conductivity of milk as an improved udder health status indicator, with no adverse effects on any of the rest of the studied milk production traits.

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

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

Association between Udder and Quarter Level Indicators and Milk Somatic Cell Count in Automatic Milking Systems.

Simple SummaryIn dairy cattle herds milked by automatic systems, the absence of a human milker originates the need for control systems to monitor the milking process and cow conditions. Modern milking robots are equipped with a lot of sensors that, at each milking (2.5–3 times a day), record data on milk yield and quality, milking efficiency, cow welfare, and health with particular focus to udder conditions. Mastitis is one of the most frequent and serious diseases of dairy cow that negatively affects milk quality and yield, reduces animal welfare, and often implies the use of antimicrobial drugs. At the moment, the alerting systems for mastitis risk is generally based on monitoring milk electrical conductivity, color, and/or temperature, but these indicators have limited reliability. Other information gathered by automatic sensors, already implemented in commercial robots, could be useful to early detect mastitis. Using a multivariate approach, our study showed that the deviations over time of milk electrical conductivity, milk yield, and milk flow of single quarters in comparison with the whole udder are potential indicators, alone or in combination, for altered udder conditions. The results could be useful for the development of new algorithms more effective in the early detection of mastitis.Automatic Milking Systems (AMS) record a lot of information, at udder and quarter level, which can be useful for improving the early detection of altered udder health conditions. A total of 752,000 records from 1003 lactating cows milked with two types of AMS in four farms were processed with the aim of identifying new indicators, starting from the variables provided by the AMS, useful to predict the risk of high milk somatic cell count (SCC). Considering the temporal pattern, the quarter vs. udder percentage difference in milk electrical conductivity showed an increase in the fourteen days preceding an official milk control higher than 300,000 SCC/mL. Similarly, deviations over time in quarter vs. udder milk yield, average milk flow, and milking time emerged as potential indicators for high SCC. The Logistic Analysis showed that Milk Production Rate (kg/h) and the within-cow within-milking percentage variations of single quarter vs. udder milk electrical conductivity, milk yield, and average milk flow are all risk factors for high milk SCC. The result suggests that these variables, alone or in combination, and their progression over time could be used to improve the early prediction of risk situations for udder health in AMS milked herds.

Influence of Calving Ease on In-Line Milk Urea and Relationship with Other Milk Characteristics in Dairy Cows

The aim of this research was to identify the relationship of calvin ease and level of in-line milk urea (MU) and other milk components, namely milk yield (MY), electrical conductivity (EC), milk fat (MF), milk protein (MP), milk fat/protein ratio (MF/MP), and somatic cell count (SCC) in dairy cows. The cows for the research were selected following such criteria: cows were tested within the period of up to the first 30 days after calving and had had a range of lactation numbers from two to four. Each selected farm housed more than 500 dairy cows and a total of 4712 calving cases from the eight dairy farms were studied and evaluated. The 4-point scale was used for the evaluation of the calving according to the point value meanings where 1 = easy, unassisted; 2 = easy, assisted; 3 = difficult, assisted; 4 = difficult, requiring veterinary intervention. A total of 4712 calving cases were researched and scored. The chemical properties of milk in all research cows were analyzed during the early phase of lactation (from the onset of calving to 30 days past calving) every day, during each milking. Cows were classified into groups according to the level of urea in milk: Group 1 had MU ≤ 15 mg/dL (12.6% of cows), Group 2 had MU 16–30 mg/dL (62.4% of cows), and Group 3 had MU &gt; 30 mg/dL (25.0% of cows). We found that cows with milk urea levels between 16 and 30 mg/dL had the lowest incidence of dystocia, and also the highest concentration of milk lactose (ML ≥ 4.6%), the lowest mean value of milk electrical conductivity, and the lowest value of milk SCC. Dystocia increased the risk of somatic cell growth in cow’s milk above the herd average (OR = 1.364; 95% CI = 1.184–1.571, p &lt; 0.001), and normal urea in milk reduced this risk (OR = 0.749; 95% CI = 0.642–0.869, p = 0.05). In all groups of cows, according to the level of urea in milk, the productivity of cows without dystocia at calving was higher (2.50–5.51 kg) as well as the milk protein % (0.13–0.21%) and milk lactose % (0.07–0.19%). We concluded that, in all groups of cows, according to the level of urea in milk, the productivity and milk lactose concentration of cows without dystocia at calving was higher, and lower somatic cells count and electrical conductivity values were found in the milk compared with cows diagnosed with dystocia at calving. We can state that dystocia has a negative effect on milk urea concentration and can increase the risk of mastitis.

Estimating milk yield for individual cows using measurements of total milk flow

In this study the ability to use a single milk flow meter that measures total milk flow in a dairy milking system to estimate milk yield for individual cows was investigated. This technology has the potential to provide estimates of the total milk yield produced over a season by individual cows that are comparable to traditional herd testing estimates. An algorithm was developed to estimate milk yield/composition for individual cows based on measurements of total milk flow in the main milk line, milking event start time, milking event duration, cow ID and bale number within the rotary milking system. The algorithm was based on the physical characteristics of the milking system, the properties of the measurement sensor and the physiological process of milk production by individual cows. The algorithm was tested using measured total flow with 3 months data (February − May) at a 60-bale rotary research farm with 772 cows milked at the start of the trial and 530 cows milked at the conclusion of the trial. The correlation between predicted (based on total flow) and measured average milk yield over the 3-month period from the 530 farm cows that were milked over the entire 3-month period was R = 0.59. The projected correlation between predicted (based on total flow) and measured average morning milk yield over a 320-day period was R = 0.83. This demonstrates that the current meter/model based on total milk flow from a 60-bale rotary milking system (with an estimated 10% error for total milk flow) provided milk yield estimates comparable to those based on two herd measurements obtained over a 320-day period (R = 0.85). Furthermore, based on the model simulations, a 2.5% error meter/model will provide milk yield and milk electrical conductivity (EC) estimates comparable to that based on six herd test measurements obtained every 60 days over a 320-day period. This demonstrates the feasibility of the single flow meter technology to estimate lactation milk yield and milk EC for individual cows.

Impact of Subclinical Mastitis-causing Bacterial Species on the Composition and Chemical Properties of Milk

Background: Here we investigate whether the composition and some chemical properties of milk can correlate with bacterial species causing subclinical mastitis in cows. Methods: One hundred and eighty cows were used in the study. The California Mastitis Test (CMT) was applied to the selected cows. The cows were divided into four groups: cows with negative CMT (n = 45), the Escherichia coli (n = 45), Streptococcus agalactiae (n = 45) and Staphylococcus aureus (n = 45) groups. Result: Milk composition and some chemical properties were measured. The highest fat ratio (5.82±0.34%) was found in the Escherichia coli group (p less than 0.05). The proportions of solid-non-fat (9.67±0.09%), protein (3.51±0.03%), lactose (5.29±05%) and mineral matter (0.75±0.01%) were lowest in the Escherichia coli group (p less than 0.05). The electrical conductivity of milk was lowest in the CMT-negative group (4.23±0.02 mS/cm), while the Streptococcus agalactiae group had the highest value (4.61±0.02 mS/cm) (p less than 0.05). The specific gravity of the Escherichia coli group was lower than the CMT-negative (1,034.91±0.56 kg/m3) and Staphylococcus aureus (1,034.24±0.65 kg/m3) groups (p less than 0.05). Based on our findings, we propose that milk composition and some chemical parameters are altered in subclinical mastitis. However, these could not be standardized according to the responsible bacterial species. We recommend that these parameters are monitored regularly on dairy farms.

Identification of Risk Factors for Lameness Detection with Help of Biosensors

In this study we hypothesized that the lameness of early lactation dairy cows would have an impact on inline biomarkers, such as rumination time (RT), milk fat (%), milk protein (%), milk fat/protein ratio (F/P), milk lactose (L, %), milk electrical conductivity of all udder quarters, body weight (BW), temperature of reticulorumen content (TRR), pH of reticulorumen content (pH), and walking activity (activity). All 30 lame cows (LCs) used in this experiment had a score of 3–4, identified according to the standard procedure of Sprecher et al. The 30 healthy cows (HC) showed a lameness score of one. RT, milk fat, MY, milk protein, F/P, L, milk electrical conductivity of all udder quarters, and BW were registered using Lely Astronaut® A3 milking robots each time the cow was being milked. The TRR, cow activity, and pH of the contents of each cow’s reticulorumen were registered using specific smaXtec boluses. The study lasted a total of 28 days. Days “−14” to “−1” denote the days of the experimental period before the onset of clinical signs of lameness (day “0”), and days “1” to “13” indicate the period after the start of treatment. We found that from the ninth day before the diagnosis of laminitis until the end of our study, LCs had higher milk electrical conductivity in all udder quarters, and higher milk fat to protein ratios. On the 3rd day before the onset of clinical signs of the disease until the day of diagnosis, the milk fat of the LC group was reduced. The activity of the LCs decreased sharply from the second day to the first day after treatment. RT in the HC group tended to decrease during the experiment. pH in LCs also increased on the day of the appearance of clinical signs.