PS2-24. Grazing Performance Testing of Beef Heifers and Bulls.

Abstract

Accurate measurement of individual dry matter intake (DMI) in grazing cattle remains a major challenge for evaluating feed efficiency traits in beef production systems. This study evaluated relationships among intake and efficiency traits measured in both pasture and drylot environments and assessed the potential for predicting pasture DMI using water intake and machine learning approaches. A total of 163 beef cattle across four experimental groups were evaluated over two grazing seasons (2024–2025). Drylot feed and water intake were measured using automated intake monitoring equipment, whereas pasture intake was estimated using a long short-term memory (LSTM) modeling approach incorporating daily water intake, body weight, animal metadata, and climatic variables. Pasture DMI was compared with observed drylot DMI using a 1:1 linear fit, and relationships among intake and performance metrics were evaluated. Average pasture DMI for heifers was 6.39 ± 0.17 kg compared with 17.30 ± 3.35 kg in the drylot, whereas bulls averaged 4.44 ± 0.02 kg on pasture and 22.30 ± 0.65 kg in the drylot. The relationship between pasture and drylot DMI exhibited substantial deviation from the 1:1 relationship, with a root mean square error of 13.63 kg. Additionally, considerable re-ranking of animals was observed for residual feed intake (RFI) and residual water intake (RWI) when comparing pasture- and drylot-derived estimates. These findings indicate that while predictive modeling approaches incorporating water intake and environmental data show promise for estimating forage intake in grazing systems, intake and efficiency relationships may differ substantially between grazing and drylot feeding environments. Precision livestock farming tools that allow accurate performance of individual, daily feed and water intake at scale have significant application to grazing research and cattle evaluation.