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.
Sci Rep. 2024 Jun 24;14(1):14557. doi: 10.1038/s41598-024-65419-0.
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.
本研究旨在利用环境和生理数据为奶牛开发一种异常体温概率（ABTP）模型。该模型旨在加强对热应激影响的管理，为农场经理提供早期预警系统，以提高奶牛福利和应对气候变化的农场生产力。该研究采用最小绝对收缩和选择算子（LASSO）算法分析了 320 头奶牛的环境和生理数据，确定了影响体温异常的关键因素。这种方法支持开发各种模型，包括 Lyman Kutcher-Burman（LKB）、Logistic、Schultheiss 和 Poisson 模型，这些模型用于评估其有效预测奶牛异常体温的能力。该研究成功验证了多种模型来预测奶牛的异常体温，重点关注温度-湿度指数（THI）作为一个关键决定因素。这些模型，包括 LKB、Logistic、Schultheiss 和 Poisson，均表现出了较高的准确性，通过 AUC 和其他性能指标（如 Brier 得分和 Hosmer-Lemeshow（HL）检验）进行衡量。研究结果突出了这些模型在捕捉奶牛热应激影响细节方面的稳健性。本研究为管理奶牛热应激开发了创新模型，有效增强了检测和干预策略。通过整合先进技术和新颖的预测模型，该研究提供了在气候变化条件下早期检测和管理异常体温的有效措施，提高了奶牛福利和农场生产力。该方法强调了使用多种模型准确预测和应对牲畜热应激的重要性，为增强农场管理实践做出了重要贡献。
