Two hundred and thirty-two cows were assigned alternately to complete dry cow therapy (infusion in all quarters on the day of drying off) or selective therapy (infusion in all quarters if a history of mastitis, California Mastitis Test score of +2 or +3 in any quarter, or if cell counts from bucket milk samples as determined by the membrane filter-deoxyribonucleic acid procedure were above 500,000 cells/ml). A dry cow product containing 10(6) units of procaine penicillin G and 1 g of dihydrostreptomycin in a slow release base was used. A 1% iodophor teat dip was used throughout the experiment. Infections of Staphylococcus aureus, Streptococcus agalactiae, other streptococci, and gram negative rods were eliminated from 85.4% of the infected quarters with complete therapy and 88.2% of the infected quarters with selective therapy. New infections occurred in 3.1% of quarters with complete therapy and in 6.5% of the quarters with selective therapy. Incidence of mastitis following the dry period was less with complete therapy compared to selective therapy (4.6% vs. 7.8% of the quarters). Selective therapy was as effective as complete therapy in eliminating existing infections. Complete therapy would be the choice in situations where new infections in dry period are of concern.

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 pre-dictive 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.

No abstract

No abstract

No abstract