Detection of oestrus in Bunaji cows under field conditions.

The objective of this study was to determine the efficacy of some aids for detecting oestrus in 72 Bunaji cows synchronized using two injections of prostaglandin F2 alpha (PGF2 alpha) 13 days apart. Blood samples to determine the accuracy of the aids and ovarian activity by radioimmunoassay technique were collected daily from the day of the second PGF2 alpha injection until oestrus or for 168 hours for 'non-responders'. The aids for detecting oestrus, tail painting, KaMar detectors and a chin-ball mating device (CMD) were applied at the time of the second injection. The oestrus response rate was 73.6% and 61.1%, from the serum progesterone concentration and visual observation, respectively. Based on the total number of oestrus events observed, unaided visual observation, tail painting, KaMar and CMD detected 52.2%, 82.6%, 82.6% and 76.8%, respectively. Visual observation of standing oestrus alone failed to detect 47.8% of the occurrence of oestrus as shown by the concentration of progesterone in the serum and 30.4%, 30.4% and 24.6%, respectively, of the oestrus periods recorded by tail painting, KaMar and CMD. Twenty-eight cows showed abnormalities in progesterone concentration. These investigations showed that oestrus may be detected by using the aids in cows that have been regarded as 'anoestrous' by visual observation of standing heat.

Application of fuzzy logic in automated cow status monitoring.

Sensors that measure yield, temperature, electrical conductivity of milk, and animal activity can be used for automated cow status monitoring. The occurrence of false-positive alerts, generated by a detection model, creates problems in practice. We used fuzzy logic to classify mastitis and estrus alerts; our objective was to reduce the number of false-positive alerts and not to change the level of detected cases of mastitis and estrus. Inputs for the fuzzy logic model were alerts from the detection model and additional information, such as the reproductive status. The output was a classification, true or false, of each alert. Only alerts that were classified true should be presented to the herd manager. Additional information was used to check whether deviating sensor measurements were caused by mastitis or estrus, or by other influences. A fuzzy logic model for the classification of mastitis alerts was tested on a data set from cows milked in an automatic milking system. All clinical cases without measurement errors were classified correctly. The number of false-positive alerts over time from a subset of 25 cows was reduced from 1266 to 64 by applying the fuzzy logic model. A fuzzy logic model for the classification of estrus alerts was tested on two data sets. The number of detected cases decreased slightly after classification, and the number of false-positive alerts decreased considerably. Classification by a fuzzy logic model proved to be very useful in increasing the applicability of automated cow status monitoring.