Modeling the Adaptation of Dairy Cows to Automatic Milking Systems Using Statistical Methods and Machine Learning: Development of the Robotic Adaptability Index.

The objectives of this study were: (1) to identify factors influencing the performance of dairy cows in an automated milking system (AMS); (2) to construct a synthetic robotic adaptability index (RAI) of cows' adaptation to the AMS; (3) to evaluate the predictive capabilities of traits describing the milking process and RAI; and (4) to compare the predictive power of different modeling approaches. The data on 796 primiparous Polish Holstein-Friesian cows (40,233 milkings) were obtained from the milking robot management system. Milking efficiency (ME), and the average number (AA) and time (AT) of the teat cup attachments and RAI served as predicted variables. Days in milk, and four AMS milking-related and 18 linear conformation traits were used as predictors. The highest predictive ability for ME was achieved with multilayer perceptron (R2 = 0.895), followed by linear regression. For AA, AT, and RAI, the highest R2 values were obtained for LASSO regression (0.663, 0.642 and 0.670, respectively). The key factors determining milking performance were functional variables, particularly milk flow rate (MilkFlow) and the number of failed milking attempts (Failure), while conformation traits had limited significance. More complex machine learning models do not always lead to improved prediction quality compared to statistical methods, which emphasizes the need for a critical approach to their application in the analysis of production data.