Prediction of Lactation Milk Yield in Simmental Cattle Milked with Robotic Milking System Using CHAID and CART Algorithms

Animal husbandry has long been a key component of agriculture, fulfilling essential nutritional needs. Technological advancements have gradually replaced human labor with machines, particularly in dairy farming, where the milking process is vital for income generation. Robotic milking systems have emerged as significant innovations, allowing for efficient, hygienic, and automated milking while reducing dependence on labor.This study aims to predict lactation milk yield (LMY) in Simmental cows during their first lactation period in robotic milking farms by using various factors, including Days in Milk (DIM), Status (S), Number of Inseminations (IN), Milk Flow Rate (MFR), Robot Rejection Rate (RRR), Rumination Time (RT), Time Spent in the Robot (TSR), Feed Amount in the Robot (FAR), Feed Consumption Rate in the Robot (FCRR), and Milking Frequency (MF). The analysis incorporates Classification and Regression Trees (C&RT) and Chi-squared Automatic Interaction Detector (CHAID) algorithms, identifying DIM as the primary predictor.The CHAID analysis revealed that newly calved cows (DIM < 30) had an average LMY of 5,692 L, while those receiving over 5.09 kg of feed achieved an average of 8,426 L. For cows in the 30 to 81 days of lactation, higher feed allocation correlated with increased milk yield. The CART algorithm confirmed these findings, establishing DIM as the most influential factor. Overall, robotic milking systems facilitate individualized management of dairy cows, optimizing factors such as feed allocation and milking frequency. By leveraging advanced algorithms to analyze these variables, this study highlights the potential for improving milk yield and animal welfare in modern dairy farming practice.