Resumen
This study addresses the challenge of subjective remounting decisions in calf and heifer rearing, typically driven by the animal caretaker?s feelings and experience, lacking a robust data foundation. Key factors such as developmental delays, diseases, or rearing problems often go unnoticed or are forgotten due to the number of animals. To address this gap, an established state-of-the-art sensor network captures behavioral data during rearing, which is supplemented by manually collected data. This facilitates a novel decision network providing well-founded recommendations to the animal owner regarding whether to retain or cull an animal. The approach focuses on four key areas: colostrum supply, milk intake, weight development, and disease history during the rearing time of each individual, offering a transparent decision path for the use of each future cow. Introducing a standardized decision-making approach, the proposed approach enables an efficient, transparent, and targeted management strategy, contributing to the sustainable enhancement of the health and performance of calves and heifers. Additionally, it allows for the comparison of the growth trajectories of different animals over time. Notably, individual and transparent decisions can be made at each growth stage, enhancing the overall decision-making process in calf and heifer rearing.