Measurements of electrical conductivity (EC) of milk are used in mastitis detection in cows due to the low cost, possibility of automation, and rapid diagnosis, but the literature about EC measurement in goats is scarce. In this study, we studied the effect of the establishment of intramammary infection (IMI) on EC of goat milk by gland using daily measurements. Additionally, the effects on milk yield, somatic cell count (SCC), and mineral content were analyzed. Eight primiparous and 10 multiparous Murciano-Granadina goats free from IMI were included in the study. Health conditions of the participating animals were monitored for 16 d and then various unfavorable health situations that may arise on commercial farms were simulated to increase the chances of IMI. Once the IMI was confirmed, the experiment continued for another 16 d. Statistical analysis was conducted using a linear mixed model considering several periods regarding the establishment of the infection and whether it affected one or both glands in the animal. The establishment of IMI caused a significant increase of EC, SCC, and chlorides in the infected glands, whereas the sodium:potassium ratio and the ratio of EC between collateral glands showed significant increases only in bilaterally infected animals. The microorganisms that caused greater increases of EC were Staphylococcus aureus and a gram-negative bacterium. Changes due to other isolated microorganisms (coagulase-negative staphylococci and streptococci) were small. No significant differences in milk yield were determined. The significant effect of infection on EC in the affected glands suggests that the use of a system based on daily readings of EC could be useful in IMI detection of goats.

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 (R = 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.

The aim of this work was to study how machine milking (MM) carried out in appropriate conditions affects teat wall thickness and canal length and their return after milking to premilking conditions compared with other milk removal methods considered biological referents: kid suckling (KS), catheter removal (CATH), and hand milking (HM). Three Latin square experiments were designed, each divided into 2 periods. In the first period, the left glands of each animal were machine milked and the KS, CATH, and HM treatments were applied to the right glands in experiments 1, 2, and 3, respectively. Subsequently, in the second period, the removal methods were interchanged. Teat wall thickness, teat wall area, teat end wall area, and teat canal length were measured from the ultrasound images. Milk removal using the reference methods (KS, CATH, and HM) and by MM caused increases in teat wall thickness and teat canal length, which were greater with MM. The time needed for the teat walls and canal to return to their physiological conditions before milk removal was greater than 10h in the reference methods and following machine milking.

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Milk electrical conductivity is employed for mastitis detection in cows due to its automation, low cost, and infection detectability at early stage. Nevertheless, the number of publications about its use in dairy goats is scarce. The aim of this study was to check and compare the detectability of goat mastitis (sensitivity and specificity) using different algorithms, constructed with individual daily conductivity data from glands, in order to improve the know how about the potential of this variable for goat mastitis detection. A total of 18 goats (8 primiparous and 10 multiparous) free of mastitis were used, and gland milk conductivity was daily monitored. After 16 days of monitoring, some unfavourable situations for gland health were simulated in order to increase the cases of infection. Once infection was established (9 goats and 12 glands got infected), the experiment continued for further 16 days. A total of 19 different algorithms that employed conductivity data from gland were designed; they were tested using gland milk conductivity (EC) and ratio of EC of collateral glands in the same goat (RAT EC ). The algorithms were tested in all the animals and intramammary infection detection ability characteristics (sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), and negative predictive value (NPV)) were recorded. All clinical cases were detected (n = 2, 100% SENS) with all the algorithms. Best global SENS (clinical and subclinical, 33.3-58.3%) and SPEC (77.8-100%) were similar to results reported in previous studies in cows, and obtained with algorithms ARIMA and Rule 1 (3 standard deviations of data). The best algorithms to use in mastitis detection depend on the prevalence and type of mastitis. EC ARIMA and Rule 1 algorithms detected the most severe cases on-line and quickly, with a low proportion of false positives.