AUTHOR=McFadden Lillian J. , Menendez Hector M. , Ehlert Krista Ann , Brennan Jameson R. , Parsons Ira L. , Olson Ken 

TITLE=Integrating multiple precision livestock technologies to advance rangeland grazing management

JOURNAL=Frontiers in Veterinary Science

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1625448

DOI=10.3389/fvets.2025.1625448

ISSN=2297-1769

ABSTRACT=Dry matter intake (DMI) of grazing animals varies depending on environmental factors and the physiological stage of production. The amount of CH4 eructated (a greenhouse gas, GHG) by ruminants is correlated with DMI and is affected by feedstuff type, being generally greater for forage diets compared to concentrates. Currently, there are limited data on the relationship between DMI and GHG in extensive rangeland systems, as it is challenging to obtain. Leveraging precision livestock technologies (PLT), data science, and mathematical nutrition models to predict DMI from enteric emission measurements of grazing cattle is likely a viable method, given the increase in available PLT for extensive systems. Therefore, our objectives were to: (1) measure CH4, CO2, and O2 emissions, DMI, and the weight of dry beef cows; (2) create a data pipeline to integrate three PLT data streams in Program R; and (3) use these data to develop a mathematical model capable of predicting grazing DMI. The predictive equation was developed using data from two feeding trials conducted using technology to measure enteric emissions, daily DMI, and front-end body weights. This study was conducted in western South Dakota with non-lactating Angus beef cows (n = 7) that received either moderate (15% crude protein, CP) or low (6% CP) quality grass hay using a 14-day adaptation period followed by a 14-day data collection period. Average CH4 (g/day), CO2 (g/day), and O2 (g/day) were 209 ± 60, 6,738 ± 1,662, and 5,122 ± 1,412 for the moderate group and 271 ± 65, 8,060 ± 1,246, and 5,774 ± 748 for the low-quality treatments, respectively. Initial models using emissions, O2 consumption, and body weight were not adequate for predicting individual DMI, with R2 values ranging from 0.01 to 0.28. Using smoothed herd-level data, the CH4 model produced the best results for predicting DMI (R2 = 0.77). This study presents a novel methodological approach to leverage data from multiple PLTs simultaneously, with the potential to advance DMI estimates for grazing cattle in rangelands.