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TECHNOLOGY AND CODE article

Front. Vet. Sci.

Sec. Animal Behavior and Welfare

Volume 12 - 2025 | doi: 10.3389/fvets.2025.1625448

This article is part of the Research TopicAdvances in Precision Livestock Management for Grazing Ruminant SystemsView all 7 articles

Integrating multiple precision livestock technologies to advance rangeland grazing management

Provisionally accepted
Lillian  McFaddenLillian McFadden1Hector  MenendezHector Menendez2*Krista  EhlertKrista Ehlert2Jameson  BrennanJameson Brennan2Ira  Lloyd ParsonsIra Lloyd Parsons2Ken  OlsonKen Olson2
  • 1North Dakota State University, Fargo, United States
  • 2South Dakota State University, Brookings, United States

The final, formatted version of the article will be published soon.

Dry matter intake (DMI) of grazing animals varies depending on environmental factors and physiological stage of production. The amount of CH4 eructated [greenhouse gases (GHG)] by ruminants is correlated to DMI and is affected by feedstuff type, being generally greater for forage diets compared to concentrates. Currently, there is 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 emissions 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 weight of dry beef cows, 2) create a data pipeline to integrate three PLT data 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 that were 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, 6738±1662, 5122±1412, and 271±65, 8060±1246, 5774±748 for the moderate and low treatments, respectively. Initial models using emissions, O2 consumption, and body weight were not adequate for predicting individual DMI, with a range of R² values of 0.01-0.28. 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 on rangelands.

Keywords: precision livestock technology, data integration, Nutrition models, Open source code, rangelands

Received: 08 May 2025; Accepted: 28 Jul 2025.

Copyright: © 2025 McFadden, Menendez, Ehlert, Brennan, Parsons and Olson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Hector Menendez, South Dakota State University, Brookings, United States

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