U.S. Department of Agriculture: Agricultural Research Service, Lincoln, Nebraska
Date of this Version
7-8-2005
Abstract
Researchers have traditionally predicted animal responses by means of statistical models. This study was conducted to evaluate modeling 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.
Comments
Published in Biosystems Engineering (2005) 91 (4), 513–524.