Comprehensive genetic analysis and predictive evaluation of milk electrical conductivity for subclinical mastitis in Chinese Holstein cows.

backgroundBovine mastitis significantly impacts the dairy industry, causing economic losses through reduced milk production, lower milk quality, and increased health risks, and early detection is critical for effective treatment. This study analyzed milk electrical conductivity data from 9,846 Chinese Holstein cows over a two-year period, collected during three daily milking sessions, alongside smart collar data and dairy herd improvement test results. The aim was to conduct a comprehensive genetic analysis and assess the potential of milk electrical conductivity as a biomarker for the early detection of bovine subclinical mastitis.

resultsThe results revealed significant phenotypic and strong genetic correlations (-0.286 to 0.457) between milk electrical conductivity, somatic cell score, milk yield, activity quantity, and milking speed. Logistic regression models yielded area under the curve values ranging from 0.636 to 0.697 and odds ratio values from 9.70 to 10.69, demonstrating a certain predictive capability of milk electrical conductivity for identifying subclinical mastitis. Various factors influencing milk electrical conductivity, including lactation stage, environmental conditions, age at first calving, parity, and body condition score, were identified. The random regression model demonstrated moderate to high heritability of milk electrical conductivity (0.458 to 0.487), particularly during the early to mid-lactation periods, with all estimates exceeding 0.35 However, after day 275 of lactation, the heritability decreased to below 0.2. Notably, shifts in genetic factors affecting milk components were observed around 60 and 270 days into lactation, with increased environmental sensitivity to milk electrical conductivity during these periods.

conclusionsThis study demonstrates that milk electrical conductivity is influenced by multiple factors, such as age at first calving, parity, and body condition score, and exhibits significant phenotypic associations with somatic cell score, milk yield, activity quantity, and milking speed. Although milk electrical conductivity showed moderate to high heritability and potential as a predictor for subclinical mastitis, its low genetic correlations with SCS limit its effectiveness as a standalone indicator. Future research should focus on combining EC with other indicators to improve the accuracy of mastitis detection.