In search of an optimal bio-logger epoch and device combination for quantifying activity budgets in free-ranging cattle

• Bio-logging devices have vast potential to improve precision livestock management • GPS and accelerometers together have greatest classification accuracy of steer behavior • Activity budgets were similar in classification schemes with and without rumination • Classification accuracy was greatest with least variation at 30 seconds • Accelerometers can achieve high classification accuracy prior to pairing with locations Precision livestock management incorporates new technologies, including bio-loggers, to remotely monitor livestock health and behavior. Despite the potential benefits in extensive cattle systems, limited adoption of these sensors has occurred potentially due to cost, technical, or processing challenges. We resampled high-resolution GPS and accelerometer data across multiple epochs (spanning 10 seconds to 15 minutes) to evaluate which combinations of devices, data features, and epochs might be considered optimal for assessing resting, grazing, travel, and rumination behavior in free-ranging cattle. We used random forest models to predict cattle behavior classified cattle behavior across the growing season to assess how variations in model accuracy were reflected in inference of activity budgets. Classification accuracy was greatest (>0.90) when GPS information was combined with at least one accelerometer metric. Epochs of 30–90 s provided the greatest classification accuracy, although epochs up to 300 s had similar classification accuracies, but with increased variability in accuracy. Classification accuracies decreased when we included rumination, but similarly had the greatest performance when both GPS and a full suite of accelerometer features was used (accuracy of ∼0.90). Average daily grazing time (8.3 h day -1 ) was within 2 hours across devices, epochs, and behavioral schemes. Rumination time was again similar across devices and epochs, averaging 6.5 h day -1 . Daily travel distance decreased by ∼4 km as the GPS fix interval increased from 10 s to 15 min. This study provides guidance for balancing fine-scale data collection with data processing and battery limitations for assessing cattle behaviors in extensive rangelands.

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Publication Details

Journal
Smart Agricultural Technology
Published
2024-11-08
DOI
https://doi.org/10.1016/j.atech.2024.100646
Citations
4
Primary Topic
Genetic and phenotypic traits in livestock
Type
article
Field-Weighted Citation Impact
2.20
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article

In search of an optimal bio-logger epoch and device combination for quantifying activity budgets in free-ranging cattle

Justin Derner, David J. Augustine, Stephanie A. Cunningham, Melanie R. Boudreau et al.
4 citations
Smart Agricultural Technology
Genetic and phenotypic traits in livestock
2.20
article

In search of an optimal bio-logger epoch and device combination for quantifying activity budgets in free-ranging cattle

Justin Derner, David J. Augustine, Stephanie A. Cunningham, Melanie R. Boudreau, David Smith
article en
4 citations

Abstract

• Bio-logging devices have vast potential to improve precision livestock management • GPS and accelerometers together have greatest classification accuracy of steer behavior • Activity budgets were similar in classification schemes with and without rumination • Classification accuracy was greatest with least variation at 30 seconds • Accelerometers can achieve high classification accuracy prior to pairing with locations Precision livestock management incorporates new technologies, including bio-loggers, to remotely monitor livestock health and behavior. Despite the potential benefits in extensive cattle systems, limited adoption of these sensors has occurred potentially due to cost, technical, or processing challenges. We resampled high-resolution GPS and accelerometer data across multiple epochs (spanning 10 seconds to 15 minutes) to evaluate which combinations of devices, data features, and epochs might be considered optimal for assessing resting, grazing, travel, and rumination behavior in free-ranging cattle. We used random forest models to predict cattle behavior classified cattle behavior across the growing season to assess how variations in model accuracy were reflected in inference of activity budgets. Classification accuracy was greatest (>0.90) when GPS information was combined with at least one accelerometer metric. Epochs of 30–90 s provided the greatest classification accuracy, although epochs up to 300 s had similar classification accuracies, but with increased variability in accuracy. Classification accuracies decreased when we included rumination, but similarly had the greatest performance when both GPS and a full suite of accelerometer features was used (accuracy of ∼0.90). Average daily grazing time (8.3 h day -1 ) was within 2 hours across devices, epochs, and behavioral schemes. Rumination time was again similar across devices and epochs, averaging 6.5 h day -1 . Daily travel distance decreased by ∼4 km as the GPS fix interval increased from 10 s to 15 min. This study provides guidance for balancing fine-scale data collection with data processing and battery limitations for assessing cattle behaviors in extensive rangelands.

Smart Agricultural TechnologyVol. 9
United States Department of Agriculture (US), Agricultural Research Service - Plains Area (US), Mississippi State University (US)
Openalex Percentile: Top 11%
Genetic and phenotypic traits in livestock
2.20
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