Abstract :
The aim of the work was to study the effect of milking fraction on electrical conductivity
of milk (EC) to improve its use in dairy goat mastitis detection using automatic EC
measurements during milking. The experiment was carried out on a group of 84 Murciano-
Granadina goats (28 primiparous and 56 multiparous). Goats were in the fourth month of
lactation. A linear mixed model was used to analyse the relationship between EC or somatic
cell count (SCC) of gland milk and parity, mammary gland health status, analysed fraction
(first 100 mL = F-1; machine milk = F-2; and stripping milk = F-3) and their first order interactions.
Additionally, the mastitis detection characteristics (sensitivity, specificity, positive
predictive value and negative predictive value) of SCC and EC were studied at different
thresholds.
All factors considered were significant for EC and SCC. EC decreased significantly as milking
progressed (from F-1 to F-3) in both healthy and infected glands. EC was not significantly
different between healthy and infected glands in F-1 and F-2 fractions, but EC of healthy
glands (5.01 mS/cm) was significantly lower than in infected glands (5.03 mS/cm) at F-3.
Mastitis detection characteristics of EC did not differ amongst studied fractions. The small
significant difference of EC between healthy and infected glands obtained in F-3 fraction did
not yield better sensitivity results compared to F-1 and F-2. The best EC mastitis detection
characteristics were obtained at 5.20 mS/cm threshold (sensitivity of 70% and specificity
of 50%). The best SCC mastitis detection characteristics were obtained at 300,000 cells/mL
threshold and F-3 fraction (sensitivity of 85% and specificity of 65%).
It was concluded that mastitis detection characteristics of EC were similar in the three
milking fractions analysed, being slightly better for SCC in F-3 fraction. As shown in previous
studies, there are no factors other than the mammary gland health status that affect milk
EC and should be considered in the algorithms for mastitis detection to improve the results.
