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Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models
Abstract
Administration of exogenous progesterone is widely used in hormonal protocols for estrous (re)synchronization of dairy cattle without regarding pharmacological issues for dose calculation. This happens because it is difficult to estimate the metabolic level of progesterone for each individual cow before administration. In the present contribution, progesterone pharmacokinetics has been determined in lactating Holstein cows with different milk production
[ver mas...]
Administration of exogenous progesterone is widely used in hormonal protocols for estrous (re)synchronization of dairy cattle without regarding pharmacological issues for dose calculation. This happens because it is difficult to estimate the metabolic level of progesterone for each individual cow before administration. In the present contribution, progesterone pharmacokinetics has been determined in lactating Holstein cows with different milk production yields. A Bayesian approach has been implemented to build two probabilistic progesterone pharmacokinetic models for high and low yield dairy cows. Such models are based on a one-compartment Hill structure. Posterior probabilistic models have been structurally set up and parametric probability density functions have been empirically estimated. Moreover, a global sensitivity analysis has been done to know sensitivity profile of each model. Finally, posterior probabilistic models have adequately recognized cow’s progesterone metabolic level in a validation set when Kullback-Leibler based indices were used. These results suggest that milk yield may be a good index for estimating pharmacokinetic level of progesterone.
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Author
Turino, Ludmila Noelia;
Cristaldi, Mariano Daniel;
Mariano, Rodolfo Nicolás;
Boimvaser, Sonia;
Scandolo Lucini, Daniel Edgardo;
Fuente
Spanish Journal of Agricultural Research 12 (2) : 396-404 (2014)
Date
2014
Editorial
Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)
ISSN
1695-971X
2171-9292
2171-9292
Formato
pdf
Tipo de documento
artículo
Palabras Claves
Derechos de acceso
Abierto
