103 Performance of a model trained on moderate-energy rations in predicting feed intake of cattle on high-energy rations.

Abstract The ability to accurately predict dry matter intake (DMI) is essential for optimizing feeding strategies in beef cattle production. This study evaluates the predictive performance of a machine learning model, GPBoost, trained on cattle receiving moderate energy rations (average NEm of 5.02 MJ/kg and NEg of 3.31 MJ/kg, Morgantown and Wardensville), to determine its accuracy in predicting DMI for animals on high energy rations (NEm 9.08 MJ/kg and NEg 6.33 MJ/kg Hy-plains). The dataset includes 44,270 observations from 607 cattle at Hy-Plains, Montezuma, KS, comprised of crossbred cattle, tested between July 6, 2022, and December 12, 2023. A regression analysis using actual DMI as the response variable and model-predicted DMI as the explanatory variable yielded an R² of 0.13, indicating low predictive power. The RMSE from the HyPlains dataset was 4.27 kg, compared to 1.44 kg in the model’s test set. Similarly, the MAE in the HyPlains dataset was 3.88 kg, significantly higher than 1.11 kg in the test set, indicating reduced prediction accuracy on high-energy rations. Additionally, the MSE for the HyPlains dataset was 18.0, compared to 2.071 in the test set of the model evaluation. Since the model’s predictive errors (MAE, MSE, and RMSE) were higher when applied to the HyPlains dataset than in the model’s test set, these findings indicate a decline in predictive accuracy when the model, trained on moderate energy rations, is applied to cattle consuming high-energy rations. In conclusion, these findings highlight the importance of collecting more accurate and comprehensive data to improve DMI predictions and enhance their reliability.