Four multiparous pregnant Nubian goats at late lactation were subjected to injection of a saponin preparation from Gypsophila roots. Saponin solution was injected into one single gland of each goat after milking 8 times. At day 2 in treatment, milk yield began to be affected, and dropped down dramatically from day 3 till dry off at day 8. The pH value of milk was increased in udder halves treated with the saponin solution after 2 days, whereas the control halves exhibited semi-plateau manner all over the experiment. Sodium concentration increased and potassium concentration decreased after 2 d in treatment. Calcium concentration in the treated udder declined after 8 d and the values of these ions were roundabout the plasma concentrations indicating that milk secreted from the treated udder halves with saponins was mostly composed of interstitial fluid.

This study explored the potential value of in-line composite somatic cell count (ISCC) sensing as a sole criterion or in combination with quarter-based electrical conductivity (EC) of milk, for automatic detection of clinical mastitis (CM) during automatic milking. Data generated from a New Zealand research herd of about 200 cows milked by 2 automatic milking systems during the 2006-2007 milking season included EC, ISCC, monthly laboratory-determined SCC, and observed cases of CM that were treated with antibiotics. Milk samples for ISCC and laboratory-determined SCC were taken sequentially at the end of a cow milking. Both samples were derived from a composite cow milking obtained from the bottom of the milk receiver. Different time windows were defined in which true-positive, false-negative, and false-positive alerts were determined. Quarters suspected of having CM were visually checked and, if CM was confirmed, sampled for bacteriological culturing and treated with an antibiotic treatment. These treated quarters were considered as gold-standard positives for comparing CM detection models. Alert thresholds were adjusted to achieve a sensitivity of 80% in 3 detection models: using ISCC alone, EC alone, or a combination of these. The success rate (also known as the positive predictive value) and the false alert rate (number of false-positive alerts per 1,000 cow milkings) were used to evaluate detection performance. Normalized ISCC estimates were highly correlated with normalized laboratory-determined SCC measurements (r = 0.82) for SCC measurements >200 x 10(3) cells/mL. Using EC alone as a detection tool resulted in a range of 6.9 to 11.0% for success rate, and a range of 4.7 to 7.8 for the false alert rate. Values for the ISCC model were better than the model using EC alone with 12.7 to 15.6% for the success rate and 2.9 to 3.7 for the false alert rate. Combining sensor information to detect CM, by using a fuzzy logic algorithm, produced a 2- to 3-fold increase in the success rate (range 21.9 to 32.0%) and a 2- to 3-fold decrease in the false alert rate (range 1.2 to 2.1) compared with the models using ISCC or EC alone. Results suggest that the performance of a CM detection system improved when ISCC information was added to a detection model using EC information.

The novel clinical mastitis detection system, based on separation of the flow and measurement of electrical conductivity from foremilk of individual udder quarters, has the potential to provide a new tool for helping farmers to monitor clinical mastitis in herds milked with conventional clusters.

No abstract