Mastitis is one the most common and costly diseases of dairy cattle, hence widely studied globally. It is the inflammation of one or more quarters of the mammary glands, mostly caused by various microorganisms. Based on whether gross changes in milk (such as watery, serous, or purulent milk, presence of clots, flakes, or blood), gross changes in the udder (such as painful or inflamed udder) and animal are seen or not, mastitis is categorized either into clinical or subclinical. With the introduction of Automatic Milking Systems (AMS), the sensors can measure milk parameters every time the cow goes for milking. These measurements are then used to identify the changes in milk parameters. Therefore, the patterns of the sensors are important to study and to check the progression of CM. Finding patterns in these sensors can aid in earlier diagnosis and the possibility of predicting the course of the disease.
A study was conducted including a total of three dairy farms run on AMS and having on average 163, 177, and 99 lactating dairy cows respectively. Two of the farms were located in the Netherlands, and one in Canada. The data was retrieved from the database of DeLaval International AB (Tumba, Stockholm). The study aimed to analyze and describe the changes in patterns of mastitis indicators recorded by sensors, before, during, and after a case of clinical mastitis (CM). The parameters studied were the somatic cell count (SCC), electrical conductivity (EC), and lactate dehydrogenase (LDH) levels of the milk for recovered and non-recovered cases. Recovery was defined based on the SCC values being less than 200,000 SCC/ml during the end of the follow-up period. The study period started from 20 days before the treatment initiation and extended to 48 days after treatment initiation. In total, 149 cases of CM were identified in the study period, out of which 91 were the first case of CM during the articular lactation. Fifty-eight of these cases recovered from CM. The statistical analyses were carried out on recovered cases with linear mixed models and results presented as estimated marginal means that were used to analyze the patterns of mastitis indicators for an episode of CM. Further, association analysis was also carried out to check the strength of the relationship between the individual mastitis indicator before and during the treatment initiation and the end of the follow-up period i.e., after 48 days of the treatment initiation.
It was found that for recovered cases, the increase in SCC values started approximately 5-8 days before achieving a peak whereas the EC values began to increase relatively later, i.e., approximately 1-4 days before attaining a peak. LDH values, for both, recovered and non-recovered cases started to increase the earliest, that is approximately 9-12 days before attaining a peak value. Furthermore, for recovered cases, it took approximately 20 days for the SCC, EC, and LDH values to stabilize after achieving a peak value. For recovered cases, the SCC and EC values took 20-24 days to drop to the pre-CM level, whereas for LDH it took up to 28 days. No significant associations between the variation in the individual mastitis indicator before CM and the recovery phase were found. It was concluded that further research with a larger dataset is needed to test whether a pre-treatment variation in SCC, EC, and LDH is of value to predict recovery from CM.
Ditsa Panchal is a veterinarian from India. She carried out her postgraduate education at the Department of Clinical Sciences at the Swedish University of Agricultural Sciences, Uppsala, Sweden. She recently obtained her Master of Science degree in Animal Science from the same university.
Text and picture: Ditsa Panchal