Analysis of the electrical conductivity in milking fractions as a mean for detecting and characterizing mastitis in goats

The Role of Ewes’ Udder Health on Echotexture and Blood Flow Changes during the Dry and Lactation Periods

Simple Summary

During lactation, the mammary glands of ewes are prone to infections caused by numerous pathogens. Consequently, mastitis and other udder abnormalities frequently occur. Unfortunately, udder inflammation, whether clinical or subclinical, it poses a significant barrier to livestock profitability by reducing milk production, decreasing milk quality, and suppressing ewes’ reproductive performance. The cost of subclinical mastitis often is greater than that of clinical mastitis. Thus, such conditions must be treated during the dry period, which is important for mammary gland repair and recovery. Moreover, an early diagnosis is crucial for effective treatment. Ultrasonography is a useful tool that is employed in the detection of echotextural and hemodynamic changes. During recent decades, there has been an increasing scientific interest in the contributions of B-mode and Doppler to the determination of physiological and pathological conditions occurring in mammary glands, which cannot be detected during a physical examination. Therefore, the aim of the present study was to investigate the alterations in ewes’ udders’ echotexture and blood flow during the end of lactation, the stages of dry period, and the first days of the postpartum period. A further objective was to investigate the hemodynamic and echotextural differentiations between ewes having healthy udders and ewes having subclinical mastitis.

Abstract

The objective of the current study was to investigate the echotextural and hemodynamic changes of ewes entering the dry period with or without subclinical mastitis. B-mode and color Doppler ultrasonography were applied to 12 Chios ewes (6 with healthy udders (group A) and 6 with subclinical mastitis (group B)) before the dry period, during the dry period (the involution phase, steady state, and transition phase), and postpartum. The color Doppler of the mammary arteries was used to evaluate them according to the pulsatility index (PI), resistive index (RI), end-diastolic velocity (EDV), time-averaged maximum velocity (TAMV), blood flow volume (BFV), and artery diameter (D). Udder parenchyma images, analyzed by Echovet v2.0, were used to evaluate the mean value (MV), standard deviation (SD), gradient mean value (GMV), gradient variance (GV), contrast (Con), entropy (Ent), gray value distribution (GVD), run length distribution (RunLD), and long run emphasis (LRunEm). In the involution phase, the PI was higher in group B compared to group A (p ≤ 0.05). The PI and RI were higher postpartum, whereas the EDV, TAVM, and D were higher in the transition phase (p ≤ 0.05). Neither the period nor the ewe group affected the MV, SD, GMV, GV, Con, and GVD values (p ≤ 0.05). In the steady state, the LRunEm was higher in group B, but postpartum, it was higher in group A (p ≤ 0.05). In conclusion, B-mode and Doppler can reveal differences (i) between healthy ewes and ewes with subclinical mastitis and (ii) among the different periods studied. Further research is needed on the blood flow and echotexture indices of the udders of ewes with unilateral subclinical mastitis.

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.

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.

Improved Fuzzy Logic System to Evaluate Milk Electrical Conductivity Signals from On-Line Sensors to Monitor Dairy Goat Mastitis

The aim of this study was to develop and test a new fuzzy logic model for monitoring the udder health status (HS) of goats. The model evaluated, as input variables, the milk electrical conductivity (EC) signal, acquired on-line for each gland by a dedicated sensor, the bandwidth length and the frequency and amplitude of the first main peak of the Fourier frequency spectrum of the recorded milk EC signal. Two foremilk gland samples were collected from eight Saanen goats for six months at morning milking (lactation stages (LS): 0–60 Days In Milking (DIM); 61–120 DIM; 121–180 DIM), for a total of 5592 samples. Bacteriological analyses and somatic cell counts (SCC) were used to define the HS of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as not healthy (NH). For each EC signal, an estimated EC value was calculated and a relative deviation was obtained. Furthermore, the Fourier frequency spectrum was evaluated and bandwidth length, frequency and amplitude of the first main peak were identified. Before using these indexes as input variables of the fuzzy logic model a linear mixed-effects model was developed to evaluate the acquired data considering the HS, LS and LS × HS as explanatory variables. Results showed that performance of a fuzzy logic model, in the monitoring of mammary gland HS, could be improved by the use of EC indexes derived from the Fourier frequency spectra of gland milk EC signals recorded by on-line EC sensors.

Evaluation of the Fourier Frequency Spectrum Peaks of Milk Electrical Conductivity Signals as Indexes to Monitor the Dairy Goats' Health Status by On-Line Sensors

The aim of this study is a further characterization of the electrical conductivity (EC) signal of goat milk, acquired on-line by EC sensors, to identify new indexes representative of the EC variations that can be observed during milking, when considering not healthy (NH) glands. Two foremilk gland samples from 42 Saanen goats, were collected for three consecutive weeks and for three different lactation stages (LS: 0–60 Days In Milking (DIM); 61–120 DIM; 121–180 DIM), for a total amount of 1512 samples. Bacteriological analyses and somatic cells counts (SCC) were used to define the health status of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as NH. For each milk EC signal, acquired on-line and for each gland considered, the Fourier frequency spectrum of the signal was calculated and three representative frequency peaks were identified. To evaluate data acquired a MIXED procedure was used considering the HS, LS and LS × HS as explanatory variables in the statistical model.Results showed that the studied frequency peaks had a significant relationship with the gland’s health status. Results also explained how the milk EC signals’ pattern change in case of NH glands. In fact, it is characterized by slower fluctuations (due to the lower frequencies of the peaks) and by an irregular trend (due to the higher amplitudes of all the main frequency peaks). Therefore, these frequency peaks could be used as new indexes to improve the performances of algorithms based on multivariate models which evaluate the health status of dairy goats through the use of gland milk EC sensors.

Short communication: Goat colostrum quality: litter size and lactation number effects

The quality of colostrum of Murciano-Granadina goats was studied to establish the transition period and the time when milk can be marketed. Forty-three dairy goats were used: 19 primiparous (15 single births; 4 multiple births) and 24 multiparous (10 single births; 14 multiple births). Samples were collected every 12h during the first week postpartum. Physicochemical parameters and somatic cell count were determined. Analysis of variance with repeated measures was used to study the effect of different factors: postpartum time, litter size, lactation number, their interactions, and production level on colostrum. Postpartum time had a significant effect on all parameters studied, which decreased along the first week of lactation, whereas lactose, pH, and conductivity increased. Based on these results, colostrum secretion takes place until 36 h postpartum (hpp). In relation to other factors of variation studied, the lactation number influenced most colostrum components, whereas the litter size only affected the pH value, protein and lactose content. The production level influenced only the protein and dry matter contents, with an inverse relationship. Milk produced during the period between 36 and 96 hpp is considered transition milk, which should not be commercialized. Milk collected after 4d postpartum (96 hpp) could be marketed, ensuring that its composition does not present a risk in the dairy industry.