PSXII-26 Identifying the core features required for accurately predicting dry matter intake in cattle using machine learning.

Abstract Accurately predicting dry matter intake (DMI) with a minimal set of input variables is crucial for optimizing feed efficiency and improving livestock management, especially since many complex input features may not be readily available to all producers. One such challenge is tracking the exact birth date of every animal, making age estimation difficult. This study investigates the essential features required for precise DMI prediction using the Gaussian Process Boosting (GPBoost) model, which integrates gradient boosting with Gaussian processes to capture structured dependencies in the data. We conducted three experiments: (1) using all available features, including climate and intake variables, (2) using a core feature set that excludes climate variables, and (3) replacing age with birth month as an independent feature to simplify data collection. Our findings reveal that the model using birth month performs comparably to models using age, demonstrating that birth month, a more accessible variable, can effectively substitute for age without compromising predictive accuracy. The mean absolute error (MAE) for the full model, core model with age, and core model with birth month are 1.10, 1.11, and 1.10, respectively. The top three important features for the full model and core model with age were body weight, age, and average daily gain to date, whereas the model using birth month identified body weight, test days, and average daily gain to date as the most influential features. These results underscore the significance of intrinsic animal characteristics over environmental factors in DMI prediction and highlight the practicality of using birth month instead of age for data-driven decision-making in cattle nutrition management.