Diurnal patterns of urination and drinking by grazing ruminants: a development in a mechanistic model of a grazing ruminant, MINDY

Measurement of water consumption and urinary nitrogen (UN) excretion of individual grazing ruminants is difficult, time-consuming and expensive. Therefore, prediction and modelling are critical for research to improve N and water use efficiency. The objective of the current work was to use a mechani...

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Bibliographic Details
Published in:The Journal of agricultural science 2018-01, Vol.156 (1), p.71-81
Main Authors: Gregorini, P., Provenza, F. D., Villalba, J. J., Beukes, P. C., Forbes, M. J.
Format: Article
Language:English
Subjects:
Agriculture
Animal lactation
Cattle
Coding
Computer simulation
Dairy cattle
Drinking behavior
Drinking water
Excretion
Grazing
Ingestion
Mathematical models
Metabolism
Modelling Animal Systems Research Paper
Nitrogen
Parameterization
Pasture management
Physiology
Science
Thirst
Urination
Urine
Urology
Water consumption
Water use
Water use efficiency
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Summary:Measurement of water consumption and urinary nitrogen (UN) excretion of individual grazing ruminants is difficult, time-consuming and expensive. Therefore, prediction and modelling are critical for research to improve N and water use efficiency. The objective of the current work was to use a mechanistic model of a grazing ruminant, MINDY, to represent drinking and urination diurnal patterns, and the resulting pattern of UN excretion. This work is primarily an integration of existing knowledge of basic urination physiology and water dynamics in ruminants. MINDY reproduces observed patterns of urination achieving the correct temporal occurrence, relative volumes and nitrogen (N) concentration of individual urination events for a grazing dairy cow, comparable with those reported in the literature. The model simulates daily water imbibed and UN realistically, as well as ingestion rates for herbages with different protein content and contrasting grazing managements. Results of a cross-validation indicate that the root mean square prediction error and mean absolute error as % of the observed mean, respectively, were 26 and 23% for daily water imbibed, 26 and 27% for urination volume, and 25 and 19% for the frequency of urination. Although further parameterization and validation are needed, for a new development in an exploratory model like MINDY, these numbers are encouraging and reflect that the concepts encoded capture many of the underlying biological mechanisms that drive the diurnal pattern and daily UN excretion, as well as thirst, acceptable.
ISSN:0021-8596
1469-5146
DOI:10.1017/S0021859617000806