PSXII-27 Adaptive dry matter intake prediction in grazing cattle using GPBoost machine learning model.

Abstract Accurately predicting dry matter intake (DMI) is crucial for optimizing livestock nutrition and management. This study develops a machine learning model using GPBoost, a hybrid gradient boosting and Gaussian process approach, to predict DMI in grazing animals based on intake-related features collected from animals housed in a drylot system. Initially, the model is trained solely on drylot data and used to predict DMI for animals in grazing conditions during summer 2021 and 2022. To improve prediction performance under grazing conditions, 250 observations of grazing data of summer 2021 is incorporated into the training set (~120,584 observations), and the model is retrained before making final predictions on unseen grazing animals’ dataset (summer 2022). Model performance is evaluated using standard evaluation metrics, and results indicate that incorporating limited grazing data enhances predictive accuracy. The root mean squared error (RMSE) has decreased from 2.95 to 2.0 when the model is trained on the drylot data added with a subset of grazing dataset. Similarly, mean absolute error (MAE), mean square error (MSE) are decreased to 1.57 and 4.44 from 2.56 and 8.75. This approach demonstrates the potential of transfer learning in livestock feeding studies and provides a framework for improving DMI estimation across different production systems.