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Evaluating Modeling Techniques for Livestock Heat Stress Prediction

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Paper number  034009,  2003 ASAE Annual Meeting . (doi: 10.13031/2013.14084) @2003
Authors:   T.M. Brown-Brandl, D.D. Jones, W. E. Woldt
Keywords:   Feedlot cattle, Heat stress, Modeling, Fuzzy inference systems, Regression, Mamdani, Sugeno

Heat stress resulting from extreme heat events has caused large death losses in feedlot cattle. A method of predicting stressful situations would aid the producer in taking proactive measures. One-hundred and twenty-eight feedlot heifers of four differing genotypes (32 of each: Angus, MARC III crossbred, Charolais, and Gelbvieh) were penned according to genotype. Respiration rates (determined by counting flank movements) and surface temperatures were taken on a randomly selected subset of 10 animals/genotype twice daily on a predetermined schedule throughout the summer of 2002. Respiration rate was used as the indicators of stress. Weather parameters were collected using a weather station located at the feedlot of interest. Four modeling techniques were used to predict respiration rate (two multiple regression, and two fuzzy inference systems). Results showed that the Sugeno type fuzzy inference system had slightly better results than the two multiple regression models, while the Mamdani type fuzzy inference system was not able to perform at the level of the other three models. It appears that all models over estimate low respiration rates and under predict high respiration rates.

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