Precision Beef Dry Matter Intake Estimation on Extensive Rangelands

Abstract Predicting dry matter intake (DMI) for beef cattle on extensive rangelands presents a significant challenge to determining stocking rates. Traditionally, DMI is estimated by taking full body weight (BW) multiplied by a percentage selected based on animal class, production phase, and forage quality, which introduces tremendous levels of accumulated error at the herd level. Animal Unit Months (AUMs) are utilized to simplify the determination of stocking rate (animal units per area per a specific period of time) of pastures. This challenge represents a tremendous opportunity to leverage precision technology to account for individual animal variation in BW and growth, with subsequent impacts on herd-level decisions. Therefore, the objective of this study was to utilize precision livestock technology (PLT) collected data to build a precision system model (PSM) to evaluate the differences in predicted DMI using either initial BW, expected mid-season BW, or PLT measured BW. The PSM model was built utilizing BW data measured using SmartScale (C-Lock Inc.) for 60 days during the summers of 2021 and 2022 on Angus yearling steers (average initial BW 393.71 ± 39.01 and 315.23 ± 53.91 kg, n = 130 and 124, for year 2021 and 2022, respectively) on native pastures at the South Dakota State University Cottonwood Field Station. The PSM evaluated total forage consumption and deterministically estimated hectares of pasture required to meet the herd forage demands relative to available biomass (kg/ha). the PSM estimated 4.49% and 6.94% more DMI at the herd level compared with using initial BW for years 2021 and 22, respectively, and 1.64% less DMI than mid-season BW in 2021. This resulted in an additional 14.03 and 17.95 ha required for years 2021 and 22, respectively, according to PSM estimates compared with initial BW, while animals were understocked by 5 and 0.3 ha for 2021 and 2022, respectively, using mid-season BW. Individuals expressed a divergent growth rate, resulting in greater misalignment between static and PSM predicted DMI as the grazing period progressed (Menendez et al., 2023), indicating greater opportunities for targeted grazing management in long grazing periods. Therefore, applying precision data provides more precise DMI estimates and demonstrates the advantages and disadvantages of herd-level estimates. The use of PSM helps to identify high-leverage precision tools to minimize a performance gap like overgrazing extensive rangeland systems and demonstrates the critical need to develop robust data-collection and processing steps to leverage continuously collected PLT data.