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August 22, 2025Frontiers in Veterinary Science0 citationsOpen Access

Integrating multiple precision livestock technologies to advance rangeland grazing management

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LMLoraine McFaddenHMHector M MenendezKEKrista Ehlert

Key Points

  • The predictive model shows a strong correlation (R^2 = 0.77) between methane emissions and dry matter intake.
  • Average methane emissions were measured at 209 g/day for moderate quality hay and 271 g/day for low quality hay.
  • Data was collected from seven non-lactating Angus beef cows over two feeding trials, utilizing a 14-day adaptation and collection period.
  • The approach integrates multiple precision livestock technologies and suggests a new direction for advancing grazing management models.

Abstract

Dry matter intake (DMI) of grazing animals varies depending on environmental factors and the physiological stage of production. The amount of CH 4 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 CH 4 , CO 2 , and O 2 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 CH 4 (g/day), CO 2 (g/day), and O 2 (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, O 2 consumption, and body weight were not adequate for predicting individual DMI, with R 2 values ranging from 0.01 to 0.28. Using smoothed herd-level data, the CH 4 model produced the best results for predicting DMI (R 2 = 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.

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Cite This Study

McFadden et al. (2025) studied this question.

synapsesocial.com/papers/68af55ccad7bf08b1eadbff4https://doi.org/10.3389/fvets.2025.1625448
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