This dataset contains all the raw data sets, processing code, and analysis for reproducing and replicating the analysis for the article: Virtual fencing in remote boreal forests: performance of commercially available GPS collars for free-ranging cattle. In total there are 21 files included, from which '01_Analysis.html' and '01_Analysis.pdf' describes the final output of all analysis and includes the figures as published in the article. '01_Analysis.qmd' is a quarto markdown file (Quarto is a multi-language, next generation version of R Markdown from Posit, see https://quarto.org/) which makes it possible to rerun the analysis. This file is dependent on the other files and the original folder structure. The dependent files include spatial information from the GPS collars ('collars.csv' and 'collars_new.csv'), measures from the differential GPS ('dGPS.csv' and 'dGPS_new.csv'), observations from field personnel ('kobo_forms.csv'), environmental information (all '.tiff' files), and other supporting information. Furthermore, data pre processing is conducted in the R-script '02_preparation_data.R' creating two output files ('processed_data_mob.txt' and 'processed_data_stat.txt'). This script can be optionally sourced from '01_Analysis.qmd'.

Article abstract:

Background

The use of virtual fencing in cattle farming is beneficial due to its flexibility, not fragmenting the landscape or restricting access like physical fences. Using GPS technology, virtual fence units emit an audible signal and a low-energy electric shock when crossing a predefined border. However, animal welfare concerns arise from potential stress and confusion caused by GPS errors. Especially in large remote grazing areas and complex terrains, where the performance of the GPS units can be affected by landscape structure, errors can lead to unnecessary shocks to the animals. This study aimed to explore factors affecting the GPS performance of commercially available virtual fence collars for cattle (NoFence©), both using static tests and mobile tests, i.e. when deployed on free-ranging cattle.

Results

The static tests revealed generally high fix success rates (% successful positioning attempts), and a lower success rate at four of 30 test locations was most likely due to a lack in GSM coverage. On average the GPS precision and accuracy errors were 3.3 m ±2.5 SD and 4.6 m ±3.2 SD, respectively. We found strong evidence that the GPS precision and accuracy errors were affected by the canopy cover, with increased errors under closed canopies. We also found evidence for an effect of the sky-view on the GPS performance, although at a lesser extent than canopy. The direction of the accuracy error in the cartesian plane was not uniform, but biased, depending on the aspect of the test locations. With an average of 10.8 m ±6.8 SD, the accuracy error of the mobile tests was more than double that of the static tests. Furthermore, we found evidence that more rugged landscapes resulted in higher GPS accuracy errors. However, the error was not affected by canopy cover, sky-view, or behaviors during the mobile tests.

Conclusions

This study showed that GPS performance can be negatively affected by landscape complexity, such as increased ruggedness and covered habitats, resulting in reduced virtual fence effectiveness and potential welfare concerns for cattle. These issues can be mitigated through proper pasture planning, such as avoiding rugged areas for the virtual fence border.

R, 4.3.2

QGIS, 3.34.4