Milk Electrical Conductivity Research Articles

Rule Discovery in Milk Content towards Mastitis Diagnosis: Dealing with Farm Heterogeneity over Multiple Years through Classification Based on Associations.

Simple SummaryInvisible (subclinical) mastitis decreases milk quality and production. Invisible mastitis is linked to an increased use of antimicrobials. The risk of the emergence of antimicrobial-resistant bacteria is a major public health concern worldwide. Therefore, early detection of infected cows is of great importance. Machine learning has opened a new avenue for early mastitis prediction based on simple and accessible milking parameters, such as milk volume, fat, protein, lactose, electrical conductivity (EC), milking time, and milking peak flow. However, farm heterogeneity is a major challenge where multiple patterns can predict mastitis. Here, we employed a classification based on associations and scaling approach for multiple pattern discovery over multiple years. The approach we have developed helps to address farm heterogeneity and generalise machine learning-based diagnosis of mastitis worldwide.Subclinical mastitis, an economically challenging disease of dairy cattle, is associated with an increased use of antimicrobials which reduces milk quantity and quality. It is more common than clinical mastitis and far more difficult to detect. Recently, much attention has been paid to the development of machine-learning expert systems for early detection of subclinical mastitis from milking features. However, differences between animals within a farm as well as between farms, particularly across multiple years, are major obstacles to the generalisation of machine learning models. Here, for the first time, we integrated scaling by quartiling with classification based on associations in a multi-year study to deal with farm heterogeneity by discovery of multiple patterns towards mastitis. The data were obtained from one farm comprising Holstein Friesian cows in Ongaonga, New Zealand, using an electronic automated monitoring system. The data collection was repeated annually over 3 consecutive years. Some discovered rules, such as when the milking peak flow is low, electrical conductivity (EC) of milk is low, milk lactose is low, milk fat is high, and milk volume is low, the cow has subclinical mastitis, reached high confidence (>70%) in multiple years. On averages, over 3 years, low level of milk lactose and high value of milk EC were part of 93% and 83.8% of all subclinical mastitis detecting rules, offering a reproducible pattern of subclinical mastitis detection. The scaled year-independent combinational rules provide an easy-to-apply and cost-effective machine-learning expert system for early detection of hidden mastitis using milking parameters.

Prediction of Reproductive Success in Multiparous First Service Dairy Cows by Parameters from In-Line Sensors

The aim of the current study was to evaluate the relationship of different parameters from an automatic milking system (AMS) with the pregnancy status of multiparous cows at first service and to assess the accuracy of such a follow-up with regard to blood parameters. Before the insemination of cows, blood samples for measuring biochemical indices were taken from the coccygeal vessels and the concentrations of blood serum albumin (ALB), cortisol, non-esterified fatty acids (NEFA) and the activities of aspartate aminotransferase (AST) and gamma glutamyltransferase (GGT) were determined. From oestrus day to seven days after oestrus, the following parameters were registered: milk yield (MY), electric milk conductivity, lactate dehydrogenase (LDH) and β-hydroxybutyric acid (BHB). The pregnancy status was evaluated using ultrasound “Easy scan” 30–35 days after insemination. Cows were grouped by reproductive status: PG− (non-pregnant; n = 48) and PG+ (pregnant; n = 44). The BHB level in PG− cows was 1.2 times higher (p < 0.005). The electrical conductivity of milk was statistically significantly higher in all quarters of PG− cows (1.07 times) than of PG+ cows (p < 0.05). The arithmetic mean of blood GGT was 1.61 times higher in PG− cows and the NEFA value 1.23 times higher (p < 0.05) compared with the PG+ group. The liver function was affected, the average ALB of PG− cows was 1.19 times lower (p < 0.05) and the AST activity was 1.16 times lower (p < 0.05) compared with PG+ cows. The non-pregnant group had a negative energy balance demonstrated by high in-line milk BHB and high blood NEFA concentrations. We found a greater number of cows with cortisol >0.0.75 mg/dL in the non-pregnant group. A higher milk electrical conductivity in the non-pregnant cows pointed towards a greater risk of mastitis while higher GGT activities together with lower albumin concentrations indicated that the cows were more affected by oxidative stress.

Influence of Calving Ease on In-Line Milk Lactose and Other Milk Components.

Simple SummaryCalving is a difficult moment in a cow’s life that causes stress, and the ease of calving determines the course of further lactation. The hypothesis of our study was to investigate how the difficulty of calving may influence changes in lactose concentration and other milk components and how well these two factors correlate between each other. We found a statistically significant (p < 0.001) negative correlation of calving ease score with milk lactose % (r = −0.376) and positive correlation coefficients with milk lactose yield (kg) (r = 0.277) as well as milk fat/lactose % ratio (r = 0.191). The analysis showed a regular increase (p < 0.001) with decreasing calving ease scores for milk electric conductivity and milk somatic cell count.The aim of our study was to determine how the ease of calving of cows may influence changes in lactose concentration and other milk components and whether these two factors correlate with each other. To achieve this, we compared data of calving ease scores and average percentage of in-line registered milk lactose and other milk components. A total of 4723 dairy cows from nine dairy farms were studied. The cows were from the second to the fourth lactation. All cows were classified according to the calving ease: group 1 (score 1)—no problems; group 2 (score 2)—slight problems; group 3 (score 3)—needed assistance; group 4 (score 4)—considerable force or extreme difficulty. Based on the data from the milking robots, during complete lactation we recorded milk indicators: milk yield MY (kg/day), milk fat (MF), milk protein (MP), lactose (ML), milk fat/lactose ratio (MF/ML), milk protein/lactose ratio (MP/ML), milk urea (MU), and milk electrical conductivity (EC) of all quarters of the udder. According to the results, we found that cows that had no calving difficulties, also had higher milk lactose concentration. ML > 4.7% was found in 58.8% of cows without calving problems. Cows with more severe calving problems had higher risk of mastitis (SCC and EC). Our data indicates that more productive cows have more calving problems compared to less productive ones.

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

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

Inline Milk Lactose Concentration as Biomarker of the Health Status and Reproductive Success in Dairy Cows

In this study, cow reticulorumen content pH and temperature together with the activity were registered using smaXtec boluses, specially designed for animal care. Body weight, rumination time, milk fat/protein ratio, milk yield, milk lactose, milk electrical conductivity, milk somatic cell count and consumption of concentrated feed were registered by Lely Astronaut® A3 milking robots. The cows in this study were assigned into two groups according to the milk lactose concentration: group 1—milk lactose &lt; 4.70% (n = 20), group 2—milk lactose ≥ 4.70% (n = 15). The following cows were further classified according to milk fat and protein ratio: F/P &lt; 1.2 (class 1), F/P = 1.2 (class 2) and F/P &gt; 1.2 (class 3). According to our results, we can conclude that inline registered milk lactose concentration can be used to indicate the health status and reproductive success of fresh dairy cows. Cows with an increased lactose concentration (≥4.70%) showed more activity (54.47%) and had less risk of mastitis (determined by lower milk electrical conductivity (EC) and somatic cell counts (SCC)) and metabolic disorders, determined by milk F/P. A higher glucose concentration was also apparent in the cows with higher lactose concentration. Registered lower levels of milk lactose can be used for early identification of metabolic disorders and mastitis (set at milk SCC ≥ 100 thousand/mL). Lactose levels in cows’ milk were positively associated with their reproductive success.

The effects of a training program using a phantom to accustom heifers to the automatic milking system

The introduction of heifers into the automatic milking system (AMS) can be associated with considerable stress for both animals and farm employees, as completely inexperienced heifers initially do not independently enter the unknown milking robot. This study investigated whether training heifers on an AMS phantom provides the possibility of preparing heifers for the following lactation at the AMS. For this purpose, 77 Holstein-Friesian heifers were randomly assigned to one of 2 experimental groups: control (CON) or phantom (PHAN). Four weeks before calving, the PHAN group was given free access to the phantom, which was similar to the actual milking robot, so that they could explore it and be positively conditioned by feeding concentrate in the phantom. The heifers of the CON group had no contact with the phantom or the AMS before the first milking at the AMS. The milking frequency per animal per day was recorded, and the proportion of animals that had to be fetched for milking was determined, to evaluate how the animals accepted the AMS after calving. To assess the stress level of the animals before and after introduction into the AMS, fecal cortisol concentrations and rumination times of the animals were measured. Additionally, lactation performance characteristics (milk yield, milk flow, electrical conductivity of milk, and milk composition) were recorded for 77 animals. The animals trained on the phantom showed a higher milking frequency (DIM 7: 2.70 ± 0.14 visits/d) than the control animals (DIM 7: 2.41 ± 0.14 visits/d) between the 4th and 10th day of lactation. In addition, between d 1 and d 5, the proportion of animals that had to be fetched for milking was lower in PHAN (DIM 1: 35.18 ± 4.16%) than in CON (DIM 1: 48.03 ± 4.46%). The PHAN heifers had unexpectedly high fecal cortisol levels (1 wk prepartum: 43.50 ± 0.93 ng/g of feces), although not considerably elevated compared with CON (1 wk prepartum: 40.76 ± 1.05 ng/g of feces). Training on the phantom had no appreciable influence on rumination time and lactation performance parameters. The increased number of milking visits and the reduced proportion of animals that had to be fetched into the AMS for milking indicate that training on the phantom prepares the animals well for being milked in the AMS. Therefore, training heifers on the phantom offers the possibility to facilitate the start into early lactation for the animals, providing a valuable contribution to improvement of animal welfare.

Relationship of milking traits and somatic cell count with electrical conductivity of goat milk during different milking phases

The aim of this study was to estimate the relation between milking traits and somatic cell count with electrical conductivity of goat milk during different milking phases. The research was carried out in the herd of Czech White Shorthaired and Saanen goat breeds (n=323) with the help of the electronic milk flow meter LactoCorder®. The milk yield, milking duration, milking flow rate and electrical conductivity of milk in the different phases of milking showed the significant mean differences between the breeds. Almost all (except electrical conductivity during the initial time) investigated indicators of electrical conductivity had a significant positive correlation with SCC. The bimodality of milk flow was determined in 9.69 % of goats and associated with milk yield decrease and SCC increase (P&amp;lt;0.05). The results confirm that the milk flow curve data is a good tool to control milking traits of goats, to predict the prevalence of mastitis and, thus, to improve the health of the udder of goats.

Flow Cytometry-Detected Immunological Markers and on Farm Recorded Parameters in Composite Cow Milk as Related to Udder Health Status.

Flow cytometry is a powerful technology used in many fields of cell biology. It is also used as a routine method to count somatic cells in milk and to characterize bovine milk leukocytes. In this study, we used flow cytometry to simultaneously assess the viability, the percentage of the single subsets of leukocytes and to quantify the expression of CD11b, an immunological marker of cell activation status. Immunological markers were then related with on farm recorded parameters as milk electrical conductivity (MEC) and average milk flow rate (MFR). Composite milk samples were collected from 43 cows, nine of which had naturally infected udders and 34 of which had no infected udders. First, the milk samples were classified according to bacteriological test in positive and negative. The results showed that the negative samples to bacteriological test had: (i) significantly higher percentages of live lymphocytes; (ii) significantly lower percentages of CD11b+ leukocytes; (iii) significantly lower MEC and higher MFR values. Then, samples were classified in three groups according to somatic cell count (SCC): Group A (n = 15), samples with SCC ≤ 100,000 cells/mL, all negative to bacteriological analysis; Group B (n = 11), samples with 100,000 < SCC < 300,000 cells/m, with four samples positive to bacteriological analysis; Group C (n = 17), samples with SCC ≥ 300,000 cell/mL with five samples positive to bacteriological analysis. Multivariate discriminant analysis was used to verify which flow cytometry immunological markers and on farm recorded parameters could better discriminate among the different groups of SCCs. Linear discriminant analysis (LDA) indicated that 5 of the 10 parameters could best be used to reveal the differences between positive and negative samples among the considered groups of SCCs. Furthermore, the Canonical discriminant analysis (CDA) indicated that composite milk samples with different SCC and infection status were clearly separated from each other in a two-dimensional space. In conclusion, the study highlighted that: (1) the conventional flow cytometry analysis applied on milk samples is a useful tool to investigate immunological parameters as potential indicators of udder health; (2) the combined evaluation of live milk leukocytes and recorded farm parameters could be useful to assess udder health status in dairy cows. The results obtained from this pilot study on few data require new and larger trials to be confirmed.

Species composition of microbiota of cows udder and raw milk quality at mastitis

To increase the productivity of cows and improve the nutritional and sanitary-technological qualities of milk is one of the most important tasks in the development of dairy farming in all countries of the world. The cause of degradation of these parameters is such a widespread disease of cows as mastitis. The social significance of mastitis is manifested in the fact that the causative agents of mastitis in cows can cause disease in humans. Since streptococci and staphylococci play a leading role in mastitis, they are the most common in milk. Studies have shown that udder disease in cows with mastitis at the beginning of lactation has a certain seasonal nature and its main peaks occur in March-May and September-November. During these months, from 2.5% to 4.1% of cows from the total number of livestock of the farm were found to have mastitis. Physiochemical features of milk from the cows with mastitis were an increase in the number of somatic cells to 1,500,000/cm3, increase of milk pH to 7.3, caused by the breakdown of milk proteins into ammonia, the increase in milk electrical conductivity to 5.92-7.54 mS/cm, associated with the entry of sodium and chlorine ions from the blood into the milk. In studies of cows with mastitis, 16 species of bacteria were isolated from the secretion of the udder. In monoculture, the microflora, that was found in 30.5% of cows, consisted of E. coli, St. epidermidis, C. freundi, Sh. Dysentery, St. aureus, St. hyicus spp. Chromogenes, Str. Agalactiae, St. lentus, and St. intermediate. In 69.5% of cows with mastitis, the microflora was found in various associations. The most common associations of bacteria were St. epidermidis + St. aureus + Str. agalactiae + Str. haemolyticus; E. coli + Str. agalactiae; Str. agalactiae + St. epidermidis; St. epidermidis + St. aureus + Str. agalactiae. We developed a method for diagnosing mastitis in cows, which was to diagnose the disease at early stage of inflammation of the mammary glands by studying the composition of the milk the examined lobe of the udder.

Association of GH, STAT5A, MYF5 gene polymorphisms with milk somatic cell count, EC and pH levels of Holstein dairy cattle

This study was conducted to ivnestigate the associations of GH-AluI, STAT5A-AvaI and MYF5-TaqI gene polymorphisms with milk somatic cell count (SCC), electrical conductivity (EC) and pH levels in Holstein dairy cows. For this purpose, 167 blood and 1670 milk samples of 167 Holstein cows in their 2nd lactation were used. There were significant relationships between GH-AluI genotypes and milk EC (p < 0.001) and between STAT5A-AvaI genotypes and milk EC (p = 0.007), but there were not any significant relationships between MYF5 gene polymorphism and the investigated traits (p > 0.05). The greatest EC values were observed in GH-AluI-LV and STAT5A-AvaI-TT-genotyped individuals. Just because of association of EC with mastitis, it was concluded that present GH-AluI and STAT5A-AvaI polymorphisms could be used in further studies to be conducted to improve mastitis resistance and milk quality traits of Holstein dairy cows.

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.

Biomarkers from automatic milking system as an indicator of subclinical acidosis and subclinical ketosis in fresh dairy cows.

The objective of this study was to determine the association between subclinical acidosis (SARA) and subclinical ketosis (SCK) with biomarkers from an automatic milking system (AMS) measuring in relation to rumination time (RT), milk yield (MY), bodyweight (BW), milk temperature, the milk fat-to-protein ratio, and the electrical conductivity of milk at the udder quarters-level which can be read in fresh dairy cows. During the course of the study, all of the fresh dairy cows (n=711) were examined according to a general clinical investigation plan. The cows were selected for 1-30 days of milk (DIM) and were milked using Lely Astronaut® A3 milking robots with free traffic. Rumination time shows a statistically significant positive correlation with milk yield (milk temperature) and is negatively correlated with the fat and protein ratio. Healthy cows demonstrated the highest level of rumination time and the lowest milk temperature. The average BW for these cows was 1.64% lower than for the SARA group and the BW kg was 2.10% higher than SCK cows. MY was 14.01% lower in comparison with SARA and 6.42% higher in comparison with SCK. According to these results, some biomarkers from the AMS have an association with SARA and SCK. However, further research with a higher number of cows is needed to confirm this conclusion.

The electrical conductivity of sheep's milk and the possibility of mastitis detection

Measurement of electrical conductivity (EC) is a method frequently used in dairy cows during milking in milking parlours, but especially in robotic milking as a low-cost mastitis detection method. The aim of this study was to evaluate the relationship between somatic cell count (SCC) and EC of milk in sheep reared in Slovakia as factors for monitoring subclinical mastitis on the basis of a bacteriological examination of udder health. Samples were collected individually from both halves of the udder from 295 sheep of different breeds from eight farms during evening milking. Based on SCC, the samples (590) were divided into classes (SCC &lt; 2 × 105, 2 × 105 ≤ SCC &lt; 4 × 105, 4 × 105 ≤ SCC &lt; 6 × 105, and SCC ≥ 6 × 105 cells.mL-1), (SCC &lt; 7 × 105 and SCC ≥ 7 × 105 cells.mL-1) and (SCC &lt; 1 × 105 and SCC ≥ 1 × 105 cells.mL-1) respectively. Based on the presence of pathogens in the udder halve, they were classified as “major pathogens” (14), “minor pathogens” (161) and “without pathogens” (415). The presence of a pathogen had a significant effect on the increase in EC, SCC and protein content and decrease in content of lactose. We found a significant correlation between EV and SCC at first classification only in cases where all data was analysed jointly (r = 0.531), SCC ≥ 6 × 105 (r = 0.403) and SCC &lt; 2 × 105 (r = 0.214). In the second and third classification, we found significant correlations in both cases, the SCC &lt; 7 × 105 (r = 0.270) and the SCC ≥ 7 × 105 (r = 0.382) and SCC &lt; 1 × 105 (r = 0.136) and the SCC ≥ 1 × 105 (r = 0.557). The electrical conductivity showed a stronger correlation with the lactose and protein content than LogSCC. We can argue that measuring the electrical conductivity of sheep milk may be a possible alternative for mastitis detection in sheep. EC can be useful in detecting animals with level of SSC greater than 6 × 105 cells.mL-1.

Milk electrical conductivity in Manchega ewes: Variation throughout milking and relation with mammary gland health status

The aim of this work was to study the effect of milking fraction and mammary gland health status on the electrical conductivity (EC) of milk from Manchega ewes, considering also the lactation number. To this end, we also studied the relationship of EC with milk macrocomposition, and the relation existing between EC and somatic cell count (SCC). Finally, the use of EC thresholds as a mastitis detection method (sensitivity, specificity, positive (PPV) and negative predictive value (NPV)) was assessed in each of the three fractions: first streams (F1), machine milk (F2) and stripping milk (F3). Milking fraction, mammary gland health status and lactation number had a significant effect on EC and SCC. In the case of EC, the milking fraction caused a more pronounced effect than health status of the glands or number of lactation (F = 19.95, 15.88 and 6.55, respectively; P &amp;lt; 0.5). In SCC, the gland health status caused the most pronounced effect followed by milking fraction and lactation number (F = 112.02, 6.89, and 5.28, respectively; P &amp;lt; 0.05). Changes in the milk composition, especially fat and lactose contents, explained the EC variation to a great extent. For the same EC threshold, specificity and sensitivity varied slightly depending on the milking fraction. NPV above 80% was obtained in the three milking fractions and at all EC thresholds tested, but PPV was only higher than 20% as of the threshold of 4.5, 4.4, and 4.2 mS/cm in F1, F2 and F3, respectively. From the results obtained, we concluded that the algorithm design for mastitis detection in sheep should include those factors affecting the composition and which therefore cause variations in EC, such as milking fraction, individual differences, lactation stage or lactation number.

The influence of different types of milking units on milk quality and safety

The article highlights comparative research results of milk quality indices obtained from the milking in specialized milking halls with such milking units as «Parallel», «Carousel» or in stalls with the milking unit «Molokoprovid». The fat and protein mass fraction, dry matter and fat-free dry matter, density, titratable and active acidity, heat resistance and freezing point have been determined according to the accepted techniques. The electrical conductivity of milk has been determined by using the analytical device MD-20 MAS-D-TEC. The total amount of milk bacteria has been determined by reductase reduction test and by seeding method in Petri dish. The milk quality has been investigated by the fermentation and rennet fermentation tests. The higher indices of the fat mass fraction, the protein mass fraction and the dry substance concentration of milk, obtained in specialized milking halls, have been established. This is not statistically significant. Milk, obtained from the milking unit «Molokoprovid», has higher index of titratable acidity, lower thermal stability in comparison with milk, obtained from specialized milking halls with milking units «Parallel» and «Carousel». It has been determined that there is the bacteria insemination increase in milk received from milking cows in stalls in comparison with milk, obtained from milking in specialized halls. Milk, obtained from the milking unit «Carousel», indicates the subclinical form of mastitis in cows or «Carousel» operation violationif there is in 1,8 mS/cm conductivity increase above average index 4,6 mS/cm. Key words: technology, quality and safety of milk, milking, milking unit, milking hall, bacterial insemination.

Фізико-хімічний склад козячого молока при дрібнотоварному виробництві в умовах природно-сільськогосподарської зони Степу України

Визначено показники молока кіз у дрібнотоварному виробництві в оптимальних кліматичних умовах Донецько-Дніпровського регіону Лівобережної провінції природно-сільськогосподарської зони Степу України. Згідно з визначенням температурно-вологісного індексу прийнятний для кіз клімат спостерігається із квітня. Середні значення та стандартні відхилення окремих фізико-хімічних параметрів молока місцевих кіз протягом лактації такі: жир 4,32 ± 1,25 %, білок 3,22 ± 0,24&nbsp;%, лактоза 4,78 ± 0,37&nbsp;%, співвідношення жир / білок 1,35 ± 0,42, сухий знежирений молочний залишок 8,67 ± 0,65&nbsp;%, густина 29 ± 3&nbsp;0А, температура замерзання -0,567 ± -0,039&nbsp;0С, електропровідність 4,49 ± 0,47 мС / см, pH 6,73 ± 0,09, кислотність 15,7 ± 1,9&nbsp;0Т. На піку лактації у молоці місцевих кіз виявили зниження вмісту білка та лактози, натомість збільшився жир. Виразність такої диспропорції проявилася в динаміці значень співвідношення жир / білок, сухого знежиреного молочного залишку та густини молока. Тому протягом перших 2–3 місяців лактації, коли молоко фактично являє собою єдине джерело поживних речовин для пластичного та енергетичного обміну козенят, концентрація лактози та білка висока. У віці 3–4 місяців козенятам доводиться задовольняти більшість своїх потреб завдяки власному пошуку корму, саме тому склад козячого молока відображає цей процес формування травної системи молодняку. Зниження депресії молока (вища точка замерзання) під час піку лактації, ймовірно, відображає зміну загальної концентрації осмотично активних речовин, особливо лактози, в цей час. Поступове підвищення електропровідності молока протягом усієї лактації викликане накопиченням електролітів, що надходять із крові тварин. Стабільний pH та кислотність молока підтверджують високу стійкість місцевих тварин до захворювань молочної залози, особливо субклінічного маститу, який спричиняє технологічне навантаження на тварин в умовах інтенсивного виробництва. Наявність 87&nbsp;% загального поголів’я кіз у власності населення як у Дніпропетровській області, так і в цілому по Україні означає, що довго залишатиметься важливим забезпечення належного рівня обслуговування дрібної худоби в окремих селянських господарствах, а не у спеціалізованих підприємствах.

Electrical conductivity of milk as related to daily milk yield of udder quarters in automatically milked cows

The aim of the study was to determine the frequency of udders with specific levels of milk electrical conductivity and milk yield in primiparous and multiparous cows. Relationships between the health and milk yield of individual udder quarters in the primiparous and multiparous cows were also analysed. The left and right front quarters constituted, respectively, 67.39 and 67.90% of the quarters from which milk with electrical conductivity ≤5.50 mS/ cm was drawn. In the conductivity class &gt;7.00 mS/cm, the right and left rear quarters showed the highest proportions (9.59 and 7.13%, respectively). Individual udder quarters from the cows most often produced either less than 7.00 kg or more than 10.00 kg milk per day. These differences concerned the front and rear udders. In the first two classes of milk electrical conductivity, the frequency of individual udder quarters in the primiparous and multiparous cows was similar. For milk with the lowest electrical conductivity, the values ranged from 50.38% (front left) to 52.62% (rear left) in primiparous cows, and from 47.38% (rear left) to 49.62% (front left) in multiparous cows. As the health of the mammary gland deteriorated, daily milk production of all its quarters was observed to decrease. A more marked reaction to this condition was observed in the front quarters, the yield of which decreased by 3.40 kg (left) and 3.02 kg (right). The rear left quarters decreased their yield by 2.85 kg/day, and the rear right ones by 2.18 kg/day. Higher production levels in all mammary quarters and in every milk electrical conductivity class were noted in the multiparous cows. The increasing milk electrical conductivity was paralleled by a lower daily milk yield of the udder quarters in both primiparous and multiparous cows. For the front left and right quarters, this decrease was 2.77 and 2.41 kg in the youngest cows, and 4.27 and 4.26 kg in the older cows. The decrease in milk production from the rear left quarters was 2.67 kg/day in primiparous and 3.76 kg/day in multiparous cows; the respective values for the rear right quarters were 2.01 and 3.86 kg milk/day.

Sensitivity of various methods (CMT, CE and Indicator Paper) of subclinical cattle’s mastitis diagnostic in some dairy cows breeding in east of Algeria

The aim of present work is to look for the prevalence of subclinical mastitis in dairy cows of different regions in eastern Algerian ; the prevalence of subclinical mastitis has called for three non-specific methods and by bacteriological analysis. On all teats are 416 ; the first non-specific test is Californiamastitis test (CMT) indicating polymorphonuclear witness infection of the udder. The second test indicates presence of ions (Na Cl) in milk and carries the appellation of the electrical conductivity of milk (EC) and the last test is the one that revealed the pH of milk while using papier pH containing pH indicator and changes color to green or blue if the udder is infected. It is the first component of the study; the second component is to get the bacteriological status of each udder and passing to the study of the sensitivity and specificity of non specifics tests . the present study gave values of mastitis prevalence between 6.7 to 64.7% of cows and 10 to 20% of udders tested by (CMT, pH papers and EC), with an infection rate of 9% of districts and 17% of cows. Bacteriological examination of positive areas showed the prevalence of the following pathogens : 6 species of Staph – Saprophyticus= 15%, 6 species staph – epidermidis=15%, 4 species staph – cohnii=10%, 9 Staph +=22.5%, 2 Micrococcus. Spp=5%, 4 E.Coli=10%, 2 speciesof klebsielle =5%,2Proteus vulgaris=5%, 2Citrobacter freundeii=5%, 1 Streptococcus spp=2.5%, 1Streptococcus uberis=2.5%. et 5% others specie of bacterias. for specificity and sensitivity we have these values r : CMT = 71% and 77%;. For pH paper had values of 15% and 60% .in latter puts the EC with values of 13% and 67% respectively. So the CMT remains the most accessible and reliable tset

Electrical conductivity and somatic cell count in zebu cow’s milk

The objective was to evaluate the physico-chemical composition, somatic cell count and electrical conductivity of the milk from lactating Zebu cows. For this, monthly milk samples were collected on the official milk control day intended for analysis of electrical conductivity of milk (ECM), somatic cell count (SCC) and milk composition. We used monthly data from 680 controls, from 10 Gyr and 17 Guzerat cows from a herd located in the city of São Gonçalo do Amarante/RN, from August to November 2013. Analyses of variance, mean test and Pearson correlation analysis were conducted. The levels of fat (% F), protein (% P), lactose (% L), total solids (% TS) and non-fat dry extract (% NFDE) were dependent variables for the ECM and SCC. The milk of Gyr cows for ECM, SCC, GOR, PROT, LACT, TS and NFDE were respectively, 3.88±1.18 mS/cm, 1629±2195 thousands cells/mL, 4.71±1.77%, 3.29±0.31%, 4.5±0.33%, 13.5±1.78%, 8.79±0.37%, and for the Guzerat breed 3.59±0.88 mS/cm 1356±1793 thousands cells/mL, 4.47±0.98%, 3.43±0.32%, 4.52±0.24%, 13.5±1.19%, 9.02±0.48%. No significant correlations between SCC and ECM characteristics were observed. However, when the correlation of each of these parameters and milk composition in different classes for each breed was analyzed, a significant correlation of the SCC was observed with the protein and lactose in Gyr, of the ECM with the lactose and SCC with total solids and fat for the Guzerat cows.

Relationship of mammary gland health status and other noninfectious factors with electrical conductivity of milk in Manchega ewes

Measuring the electrical conductivity (EC) of milk during milking has been extensively studied in cattle as a low-cost mastitis detection method that can be easily automated. The aim of this work was to study the effect of the health status of the glands and several noninfectious factors (lactation stage, milking session, and lactation number) that affect the use of EC measurement of milk to detect mastitis in dairy sheep livestock. Likewise, we studied the relation between EC and milk composition (macrocomposition and mineral content) and between EC and somatic cell count (SCC). Finally, we evaluated the use of EC thresholds as a mastitis detection method. To this end, we monitored the glandular milk EC throughout 2 consecutive lactations, during which 42 and 40 ewes were controlled, respectively. We carried out 7 biweekly checks, analyzing the EC, SCC, composition, and mineral content of glandular milk at morning and evening milkings. Before the morning milking, samples were aseptically collected for bacteriological analysis, and the results along with the SCC were used to classify the glands according to their sanitary status (healthy, latently infected, or infected). Lactation stage, parity, milking (morning or evening), health status, and the interactions of parity with health status, lactation stage with health status, and parity with lactation stage all had a significant effect on SCC and EC of the milk. The correlation between EC and SCC was only significant when all the data were analyzed jointly (r = 0.33) and for SCC ≥ 600.000 cells/mL (r = 0.25). The changes in milk composition, mainly in fat content, largely explained the variation in EC (R2 = 0.69). For the same EC threshold, the specificity and sensitivity varied depending on the parity or the milking, with the negative predictive value obtained being higher than the positive predictive value at all times. We concluded that developing methods of detecting mastitis in sheep by milk EC readings would require consideration of noninfectious factors that also affect the gauging of EC. One option to consider would be individualized daily monitoring of the glands, as demonstrated in other species such as cattle and goat.