Establishment and risk factor assessment of the abnormal body temperature probability prediction model (ABTP) for dairy cattle.

Autor: Lee TF; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.; Medical Physics and Informatics Laboratory of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.; Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.; PhD Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.; School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan., Chiu CL; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan., Liu YH; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan., Chang CH; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan., Shao JC; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.; Medical Physics and Informatics Laboratory of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan., Guo SS; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.; Medical Physics and Informatics Laboratory of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan., Liao YL; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan., Chen CH; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan., Tseng CD; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.; Medical Physics and Informatics Laboratory of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan., Chao PJ; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan. pjchao99@gmail.com.; Medical Physics and Informatics Laboratory of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan. pjchao99@gmail.com., Lee SH; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan. leeshenhao@gmail.com.; Medical Physics and Informatics Laboratory of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan. leeshenhao@gmail.com.; Department of Radiation Oncology, Linkou Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Linkou, Taiwan. leeshenhao@gmail.com.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Jun 24; Vol. 14 (1), pp. 14557. Date of Electronic Publication: 2024 Jun 24.
DOI: 10.1038/s41598-024-65419-0
Abstrakt: The study aims to develop an abnormal body temperature probability (ABTP) model for dairy cattle, utilizing environmental and physiological data. This model is designed to enhance the management of heat stress impacts, providing an early warning system for farm managers to improve dairy cattle welfare and farm productivity in response to climate change. The study employs the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to analyze environmental and physiological data from 320 dairy cattle, identifying key factors influencing body temperature anomalies. This method supports the development of various models, including the Lyman Kutcher-Burman (LKB), Logistic, Schultheiss, and Poisson models, which are evaluated for their ability to predict abnormal body temperatures in dairy cattle effectively. The study successfully validated multiple models to predict abnormal body temperatures in dairy cattle, with a focus on the temperature-humidity index (THI) as a critical determinant. These models, including LKB, Logistic, Schultheiss, and Poisson, demonstrated high accuracy, as measured by the AUC and other performance metrics such as the Brier score and Hosmer-Lemeshow (HL) test. The results highlight the robustness of the models in capturing the nuances of heat stress impacts on dairy cattle. The research develops innovative models for managing heat stress in dairy cattle, effectively enhancing detection and intervention strategies. By integrating advanced technologies and novel predictive models, the study offers effective measures for early detection and management of abnormal body temperatures, improving cattle welfare and farm productivity in changing climatic conditions. This approach highlights the importance of using multiple models to accurately predict and address heat stress in livestock, making significant contributions to enhancing farm management practices.
(© 2024. The Author(s).)
Databáze: MEDLINE
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