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The evaluation of a dynamic, mechanistic, thermal balance model for Bos indicus and Bos taurus

Published online by Cambridge University Press:  28 August 2013

V. A. THOMPSON ,
R. D. SAINZ ,
A. B. STRATHE ,
T. R. RUMSEY  and
J. G. FADEL
V. A. THOMPSON
Affiliation:
Department of Animal Science, University of California, Davis, CA, USA
R. D. SAINZ
Affiliation:
Department of Animal Science, University of California, Davis, CA, USA
A. B. STRATHE
Affiliation:
Department of Animal Science, University of California, Davis, CA, USA
T. R. RUMSEY
Affiliation:
Department of Animal Science, University of California, Davis, CA, USA
J. G. FADEL*
Affiliation:
Department of Animal Science, University of California, Davis, CA, USA
*
*To whom all correspondence should be addressed. Email: jgfadel@ucdavis.edu
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Summary

The Thompson model (Thompson et al., in press), a heat balance model for cattle, was evaluated for Bos indicus and B. taurus under different climate conditions through the use of two local and one global sensitivity analyses and tested against independent datasets. The local analyses, which evaluate the individual effects of parameters on model output, showed that the vasodilation/vasoconstriction parameter and reference body temperature (Tbref) strongly affected body temperature. The global analysis, which evaluates the overall effect of parameters on model output, showed that 6 out of 24 parameters account for 0·79–0·89 of the model variation. The high proportion of variation accounted for by the parameters demonstrates that the model is linear in its parameters, with little interaction between the parameters.

The Thompson model was tested against four independent datasets which included both B. indicus and B. taurus animals. The prediction of the relationship between skin and body temperature from the model aligned closely with the relationship in the datasets (R2 ranged from 0·55 to 0·87, mean bias ranged from 0·32 to 1·49). The prediction of sweating and respiration rates from the model aligned closely with the rates measured in the datasets (R2 ranged from 0·80 to 0·98 and 0·79 to 0·93, respectively). The delay in the diurnal body temperature variation, relative to air temperature, was more accurately predicted for cattle in the sun than for cattle in climate chambers. Given the limited datasets for construction and parameterization (both of which are described in Thompson et al., in press), the model evaluated in the current study performed relatively well compared to the literature and known biology.

Type
Modelling Animal Systems Research Papers
Information
The Journal of Agricultural Science , Volume 152 , Issue 3 , June 2014 , pp. 483 - 496
Copyright
Copyright © Cambridge University Press 2013 

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References

REFERENCES

Allen, T. (1962). Responses of Zebu, Jersey, and Zebu X Jersey crossbred heifers to rising temperature, with particular reference to sweating. Australian Journal of Agricultural Research 13, 165–179.Google Scholar
Bibby, J. & Toutenburg, H. (1977). Prediction and Improved Estimation in Linear Models. Chichester: Wiley.Google Scholar
Brown-Brandl, T. M., Nienaber, J. A., Eigenberg, R. A., Hahn, G. L. & Freetly, H. (2003). Thermoregulatory responses of feeder cattle. Journal of Thermal Biology 28, 149–157.Google Scholar
Brown-Brandl, T. M., Eigenberg, R. A., Nienaber, J. A. & Hahn, G. L. (2005). Dynamic response indicators of heat stress in shaded and non-shaded feedlot cattle, Part 1: analyses of indicators. Biosystems Engineering 90, 451–462.Google Scholar
Finch, V. A. (1985). Comparison of non-evaporative heat transfer in different cattle breeds. Australian Journal of Agricultural Research 36, 497–508.Google Scholar
Finch, V. A. (1986). Body temperature in beef cattle: its control and relevance to production in the tropics. Journal of Animal Science 62, 531–542.Google Scholar
Kibler, H. H. & Yeck, R. G. (1959). Environmental Physiology and Shelter Engineering with Special Reference to Domestic Animals L: Vaporization Rates and Heat Tolerance in Growing Shorthorn, Brahman and Santa Gertrudis Calves Raised at Constant 50° and 80°F Temperatures. Missouri Research Bulletin 701. Columbia, MO: University of Missouri, College of Agriculture Agricultural Experiment Station.Google Scholar
MATLAB (2010). Matlab Version 7.8.0. Natick, MA: MathWorks Inc.Google Scholar
McArthur, A. J. (1987). Thermal interaction between animal and microclimate: a comprehensive model. Journal of Theoretical Biology 126, 203–238.Google Scholar
McGovern, R. E. & Bruce, J. M. (2000). A model of the thermal balance for cattle in hot conditions. Journal of Agricultural Engineering Research 77, 81–92.Google Scholar
R Development Core Team (2010). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008). Global Sensitivity Analysis. The Primer. Chichester, UK: John Wiley and Sons, Ltd.Google Scholar
Sprinkle, J. E., Holloway, J. W., Warrington, B. G., Ellis, W. C., Stuth, J. W., Forbes, T. D. A. & Greene, L. W. (2000). Digesta kinetics, energy intake, grazing behavior, and body temperature of grazing beef cattle differing in adaptation to heat. Journal of Animal Science 78, 1608–1624.Google Scholar
Thompson, V. A., Barioni, L. G., Rumsey, T. R., Fadel, J. G. & Sainz, R. (in press). The development of a dynamic, mechanistic, thermal balance model for Bos indicus and Bos taurus. Journal of Agricultural Science, Cambridge (in press).Google Scholar
Thompson, V. A., Fadel, J. G. & Sainz, R. D. (2011). Meta-analysis to predict sweating and respiration rates for Bos indicus, Bos taurus, and their crossbreds. Journal of Animal Science 89, 3973–3982.Google Scholar
Turanyi, T. (1990). Sensitivity analysis of complex kinetic systems – tools and applications. Journal of Mathematical Chemistry 5, 203–248.Google Scholar
Turnpenny, J. R., McArthur, A. J., Clark, J. A. & Wathes, C. M. (2000). Thermal balance of livestock 1. A parsimonious model. Agricultural and Forest Meteorology 101, 15–27.Google Scholar