Modelling of heat stress in a robotic dairy farm. Part 2: identifying the specific thresholds with production factors
Article
Article Title | Modelling of heat stress in a robotic dairy farm. Part 2: identifying the specific thresholds with production factors |
---|---|
ERA Journal ID | 3455 |
Article Category | Article |
Authors | Ji, Boyu (Author), Banhazi, Thomas (Author), Ghahramani, Afshin (Author), Bowtell, Les (Author), Wang, Chaoyuan (Author) and Li, Baoming (Author) |
Journal Title | Biosystems Engineering |
Journal Citation | 199, pp. 43-57 |
Number of Pages | 15 |
Year | 2020 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 1537-5110 |
1537-5129 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.biosystemseng.2019.11.005 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1537511019308797 |
Abstract | Thresholds of heat stress are identified by determining the values of thermal comfort indices with significant change of animal responses. However, published thresholds may lead to inaccuracy when dealing with specific climate conditions, animal breeds and production factors. Thus, determining dynamic thresholds might provide better assessment of heat stress, with self-calibration capabilities. In this study, a large data set of individual age,body mass (BM), days in milk (DIM), daily milk yield (DMY) and milk temperature (MT) of 126 lactating Holstein cows was collected from a robotic dairy farm over five years. The ambient temperature data was collected from a local weather station and processed as daily minimum and mean temperature (Tmin and Tmean). For the whole herd, a new series of heat stress thresholds with stages were defined as comfort stage, milk heat stress,effective heat stress and critical heat stress. The definition was based on the cow’s responses in DMY and MT, which provides a potential approach to accurately alert for heat stress in robotic farming systems by using the existing data source. For the specific individuals, dynamic thresholds of heat stress were identified and categorised using the decision tree machine learning model. The categorisation achieved 79-94% overall accu-racy, and demonstrated the importance of cooling cows during their early lactation period. |
Keywords | robotic dairy farming, heat stress threshold, decision tree classification, modelling |
ANZSRC Field of Research 2020 | 300399. Animal production not elsewhere classified |
300207. Agricultural systems analysis and modelling | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Mechanical and Electrical Engineering |
Centre for Sustainable Agricultural Systems | |
China Agricultural University, China | |
Institution of Origin | University of Southern Queensland |
Funding source | Grant ID research scholarship granted by the University of Southern Queensland |
https://research.usq.edu.au/item/q5827/modelling-of-heat-stress-in-a-robotic-dairy-farm-part-2-identifying-the-specific-thresholds-with-production-factors
203
total views9
total downloads1
views this month0
downloads this month