Modeling milk yield in dairy cows is essential for improving management decisions, but traditional lactation curve models often fail to capture individual variability. Machine learning approaches offer greater flexibility; however, their performance in small, within-herd datasets and their reliance on explicit cow identification remain unclear, particularly in grazing systems. This study aimed to evaluate whether routinely measured biological traits can substitute for cow identification in machine learning models for predicting daily milk yield within a herd under limited data conditions. The dataset comprised 62 lactations from 48 Holstein–Friesian cows in a grazing system. Two machine learning models were developed: one including cow identification (With ID) and another excluding cow identification but incorporating milk quality traits, body weight, and body condition score (Without ID). Both models were compared with the Wood lactation model fitted to individual cows. The With ID and Without ID models achieved R2 values of 0.97 and 0.93 and RMSE values of 1.2 and 1.6 kg d−1, respectively. Both machine learning models outperformed the Wood model fitted individually to each cow (R2 2.03 kg d−1), which represents an implicitly cow-specific approach. The model including cow identification therefore served as a machine learning analogue to this benchmark. Importantly, the trait-based model closely matched the performance of the cow-specific model. These results demonstrate that machine learning models based on routinely measured traits provide a practical approach for predicting within-herd milk yield from small datasets, while retaining much of the accuracy of cow-specific models.
Guevara-Escobar et al. (2026) studied this question.