The length and onset of estrus was studied in 71 lactating dairy cows using an electronic heatmount sensor (HeatWatch; DDx Inc., Boulder, CO, DeForest, WI) and an electronic activity tag (Heat Seeker, Boumatic, Madison, WI). Three methods were used to determine estrus: 1) the electronic heatmount system, 2) an increased activity ratio algorithm determined by the Heat Seeker, and 3) an increased activity count algorithm calculated for each estrous period. Mounting and physical activity variables were characterized, and the effects of synchrony, parity, and weather on these variables were determined with data from two different trials. Cows in trial 1 were not synchronized, while cows in trial 2 were synchronized. The results of the study were consistent as follows: mean numbers of mounts were 6.70 +/- 0.7 and 5.42 +/- 0.80 for trials 1 and 2, respectively; each mount lasted 3.20 +/- 0.19 s (trial 1) and 3.36 +/- 0.42 s (trial 2). Total mounting activity averaged 5.83 +/- 0.78 h per estrous period in trial 1 and 5.57 +/- 1.02 h in trial 2. Estrus identified by the increased activity count algorithm corresponded more closely to standing mount activity (determined by the HeatWatch System) than did the increased activity ratio algorithm. Synchrony, parity, and weather did not have a direct effect on physical activity. Hot weather decreased the duration of standing mount activity significantly, but did not affect the number or duration of individual mounts. All three methods of estrus detection employed improved the efficiency of detection over visual observation.

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.

Efficacy of detecting subclinical mastitis by electrical conductivity of milk was compared with that of other indirect methods including chloride, sodium, potassium, lactose, bovine serum albumin, and somatic cell count of milk. Quarter samples of foremilk, strippings, and bucket milk were obtained from 75 cows at the afternoon milking over 8 wk. Infection of quarters was ascertained by bacteriological analysis. Electrical conductivity, chloride, and sodium content of milk were more accurate for predicting infection status of quarters than were other variables. Most variables were more accurate in predicting infection when measures were in strippings rather than in foremilk or bucket milk. For measures in strippings, misclassifications by electrical conductivity were 11.2 and 15.5% for false positives and false negatives. The accuracy of the electrical conductivity of milk for detection of subclinical mastitis compared favorably with all indirect methods. Accuracy of detection and adaptability to both manual and automatic cow-side mastitis detection systems indicate that the method has considerable potential as a screening test for subclinical mastitis.

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.