A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level

Abstract Milk production in dairy cow udders is a complex and dynamic physiological process that has resisted explanatory modelling thus far. The current standard model, Wood’s model, is empirical in nature, represents yield in daily terms, and was published in 1967. Here, we have developed a dynami...

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Main Authors: Patrick Gasqui, Jean-Marie Trommenschlager
Format: Article
Language:English
Published: Nature Publishing Group 2017-08-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-017-09322-x
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spelling doaj-e2c446293b9a4c52ab1cdcea7b3713592020-12-08T01:12:58ZengNature Publishing GroupScientific Reports2045-23222017-08-017111110.1038/s41598-017-09322-xA new standard model for milk yield in dairy cows based on udder physiology at the milking-session levelPatrick Gasqui0Jean-Marie Trommenschlager1Auvergne – Rhône-Alpes Center, Animal Health Division, National Institute for Agricultural Research, Animal Epidemiology Research Unit, EPIA, INRANancy-Lorraine Center, Science for Action and Development Division, National Institute for Agricultural Research, Mirecourt FarmAbstract Milk production in dairy cow udders is a complex and dynamic physiological process that has resisted explanatory modelling thus far. The current standard model, Wood’s model, is empirical in nature, represents yield in daily terms, and was published in 1967. Here, we have developed a dynamic and integrated explanatory model that describes milk yield at the scale of the milking session. Our approach allowed us to formally represent and mathematically relate biological features of known relevance while accounting for stochasticity and conditional elements in the form of explicit hypotheses, which could then be tested and validated using real-life data. Using an explanatory mathematical and biological model to explore a physiological process and pinpoint potential problems (i.e., “problem finding”), it is possible to filter out unimportant variables that can be ignored, retaining only those essential to generating the most realistic model possible. Such modelling efforts are multidisciplinary by necessity. It is also helpful downstream because model results can be compared with observed data, via parameter estimation using maximum likelihood and statistical testing using model residuals. The process in its entirety yields a coherent, robust, and thus repeatable, model.https://doi.org/10.1038/s41598-017-09322-x
collection DOAJ
language English
format Article
sources DOAJ
author Patrick Gasqui
Jean-Marie Trommenschlager
spellingShingle Patrick Gasqui
Jean-Marie Trommenschlager
A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level
Scientific Reports
author_facet Patrick Gasqui
Jean-Marie Trommenschlager
author_sort Patrick Gasqui
title A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level
title_short A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level
title_full A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level
title_fullStr A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level
title_full_unstemmed A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level
title_sort new standard model for milk yield in dairy cows based on udder physiology at the milking-session level
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2017-08-01
description Abstract Milk production in dairy cow udders is a complex and dynamic physiological process that has resisted explanatory modelling thus far. The current standard model, Wood’s model, is empirical in nature, represents yield in daily terms, and was published in 1967. Here, we have developed a dynamic and integrated explanatory model that describes milk yield at the scale of the milking session. Our approach allowed us to formally represent and mathematically relate biological features of known relevance while accounting for stochasticity and conditional elements in the form of explicit hypotheses, which could then be tested and validated using real-life data. Using an explanatory mathematical and biological model to explore a physiological process and pinpoint potential problems (i.e., “problem finding”), it is possible to filter out unimportant variables that can be ignored, retaining only those essential to generating the most realistic model possible. Such modelling efforts are multidisciplinary by necessity. It is also helpful downstream because model results can be compared with observed data, via parameter estimation using maximum likelihood and statistical testing using model residuals. The process in its entirety yields a coherent, robust, and thus repeatable, model.
url https://doi.org/10.1038/s41598-017-09322-x
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