Impatto di sistemi AMS e strumenti di zootecnia di precisione sulla gestione della mastite negli allevamenti di bovini da latte

Abstract

With the increased specialization of livestock farms observed in recent decades, efforts have been made to maximize milk production and the reduction of costs associated with farm management. However, a worsening of reproductive parameters (longer calving-conception period, reduction of average lactations per cow and increased susceptibility to diseases) has been observed. Particularly for infectious diseases such as mastitis, there has been an increase in the frequency of this disease condition in high-producing cows over the years. Mastitis control in the dairy sector is crucial not only for farm economics, but also for animals. Proper animal management and new technologies (Precision Livestock Farming), can help to control and identify more mastitis cases. GEA Farming Technologies has developed the DairyMilk M6850 cell count sensor, which allows the number of somatic cells in milk to be assessed at quarter level. Our study aims to evaluate the reliability of this system by comparing the data processed by the sensor with those obtained through the use of the California Mastitis Test (CMT). The protocol used for data collection involved quarter milk sampling all animals at "day 0". CMT was done on the cows with the exception of cows in the colostrum stage. Subsequently, only cows showing at least one cell alarm on the GEA software were sampled with CMT during the experimental period. By analyzing the collected data related to cell alarms and comparing them with the CMT results, it was shown that the GEA DairyMilk M6850 has very good specificity (92%) and fair sensitivity (61.8%). In addition, conductivity data were analyzed by analysis of variance. This analysis confirms the ability of this parameter to recognize milk produced by a healthy quarter (CMT category N) from milk produced by mastic one (CMT categories T, 1, 2 and 3). However, the system fails to differentiate the categories of CMT T and 1. To improve this value, further experimental studies could be carried out on the GEA sensor by going to modify the data collection protocol and make it more accurate. In addition, the protocol used could be extended to more farms at the same time so that the reliability of this instrument could be evaluated in different realities. This could also help us to understand the different impact of the sensor on realities that use different management methods and thus understand whether there are types of farms that are more suitable for implementing a system such as the GEA DairyMilk M6850.