Mastitis-related traits’ heritability: a comparison of the evaluation between functional controls and Automatic Milking Systems

Mastitis is one of the most important diseases in dairy farm, reducing milk production and causing heavy economic losses [1]. One of the possible approaches to reduce its impact is to genetically select disease-resistant cows, which is possible even at early stages of life (e.g., heifers) [2]. However, few steps are required before being able to exploit genomic selection, including the identification of phenotypes of interest and the evaluation of their heritability (h2). By definition, h2 is relative to the specific animal population in which it is evaluated and is highly influenced by the measurements of the phenotypes involved. Therefore, the technologies and measurement techniques used to record data should be carefully selected. Traditionally, milk and mastitis-related traits are measured by monthly milk composition analyses (MCA) in which different phenotypes (i.e., milk yield, somatic cell count, and fat percentage) are measured and are therefore considered as a gold standard measurements. Nowadays, however, Automatic Milking Systems (AMS) are increasingly available in commercial dairy farms and can record the same traits as the functional controls on a daily basis. Able to record a high amount of data (the so-called big data technology), AMS could be a valuable helping hand in measuring phenotypes in veterinary and animal sciences. The measurement technology of the two strategies is, however, not the same. In this study we compared the h2 evaluated on different milk and mastitis-related traits measured by MCA and AMS in 5 Holstein Friesian dairy farms in a 6.35 years’ time period (18,813 observations, 1810 cows). Correlations between the same milk-related trait measured with both MCA and AMS were calculated. Lastly, pedigree-based h2 of the studied traits from the two different strategies was evaluated (using the breedR package for R) and then compared for each couple of traits. Milk yield h2 was similar comparing the two measurement technologies (28% AMS vs 25.3% MCA). Differently, h2 of the other milk-related traits differed when estimated on data from the two involved technologies. The results obtained in this preliminary study confirmed the importance of the methods used in phenotypes recording. [1] P.L. Ruegg, “A 100-year review: mastitis detection, management, and prevention” J. Dairy. Sci, vol. 100, pp. 10381–10397, 2017. [2] R. Moretti, et al., “A practical application of genomic predictions for mastitis resistance in Italian Holstein Heifers” Animals, vol. 12, 2370, 2022.