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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2017-08-01
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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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