期刊
ANIMAL PRODUCTION SCIENCE
卷 54, 期 10, 页码 1796-1804出版社
CSIRO PUBLISHING
DOI: 10.1071/AN14409
关键词
alkanes; cattle; chromic oxide; goats; phenomics; sheep
资金
- CSIRO Animal Food and Health Sciences
- NSW Department of Primary Industries
- CSIRO Computational Informatics
- University of New England
Wireless sensor networks (WSN) offer a novel method for measuring important livestock phenotypes in commercial grazing environments. This information can then be used to inform genetic parameter estimation and improve precision livestock management. Arguably, these technologies are well suited for such tasks due to their small, non-intrusive form, which does not constrain the animals from expressing the genetic drivers for traits of interest. There are many technical challenges to be met in developing WSN technologies that can function on animals in commercial grazing environments. This paper discusses the challenges of the software development required for the collection of data from multiple types of sensors, the management and analyses of the very large volumes of data, determination of which sensing modalities are sufficient and/or necessary, and the management of the constrained power source. Assuming such challenges can be met however, validation of the sensor accuracy against benchmark data for specific traits must be performed before such a sensor can be confidently adopted. To achieve this, a pasture intake research platform is being established to provide detailed estimates of pasture intake by individual animals through chemical markers and biomass disappearance, augmented with highly annotated video recordings of animal behaviours. This provides a benchmark against which any novel sensor can be validated, with a high degree of flexibility to allow experiments to be designed and conducted under continually differing environmental conditions. This paper also discusses issues underlying the need for new and novel phenotyping methods and in the establishment of the WSN and pasture intake research platforms to enable prediction of feed intake and feed efficiency of individual grazing animals.
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