Author ORCID Identifier

https://orcid.org/0009-0008-1259-1078

Semester

Spring

Date of Graduation

2026

Document Type

Thesis

Degree Type

MS

College

Davis College of Agriculture, Natural Resources and Design

Department

Animal and Nutritional Sciences

Committee Chair

Matthew E Wilson

Committee Member

Troy Rowan

Committee Member

Ibukun Ogunade

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 in one of two grazing seasons (2024 or 2025). Drylot feed and water intake were measured using automated intake monitoring equipment, whereas pasture intake was determined 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 assessment of individual, daily feed and water intake at scale have significant application to grazing research and cattle evaluation.

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