Evaluating Modelling Techniques for Cattle Heat Stress Prediction

• Published in: Biosystems engineering Vol. 91; no. 4; pp. 513 - 524
• Main Authors: Brown-Brandl, T.M., Jones, D.D., Woldt, W.E.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.08.2005; Elsevier
• Subjects: Agricultural machinery and engineering; Agronomy. Soil science and plant productions; Animal productions; Biological and medical sciences; breathing; breeds; cows; environmental factors; equations; Fundamental and applied biological sciences. Psychology; Generalities. Biometrics, experimentation. Remote sensing; heat stress; heifers; neural networks; prediction; skin temperature; statistical models; Terrestrial animal productions; Vertebrates; Thermal stress; Bovine; Ruminant animal; Prediction; Modeling; Stress; Biological system; Engineering; Vertebrata; Mammalia; Thermal shock; Agriculture; Artiodactyla; Technique; Ungulata
• ISSN: 1537-5110; 1537-5129
• DOI: 10.1016/j.biosystemseng.2005.04.003

Researchers have traditionally predicted animal responses by means of statistical models. This study was conducted to evaluate modelling techniques. One hundred and twenty-eight feedlot heifers were observed during a 2-month period during the summer of 2002. Respiration rate and surface temperature were taken on a random sample of 40 animals twice a day. Five different models (two statistical models, two fuzzy inference systems, and one neural network) were developed using 70% of this data, and then tested using the remaining 30%. Results showed that the neural network described the most variation in test data (68%), followed by the data-dependent fuzzy model (Sugeno type) (66%), regression models (59 and 62%), while the data-free fuzzy model (Mamdami type) described only 27%. While the neural-network model may be a slightly better approach, the researcher may learn more about responses using a fuzzy inference system approach. For all models tested, respiration rate is over-predicted at low stress conditions and under-predicted at high stress conditions. This suggests that all models are lacking a key piece of input data, possibly the accumulative effects of prior weather conditions, to make an accurate prediction.