Modeling efficiency and robustness in ruminants: the nutritional point of view.

The efficiency and robustness of farm animals has been of growing interest in recent decades, particularly for ruminants which are subject to many constraints. In recent times, systemic modeling approaches have been developed with promising applications in all areas of livestock production.Therefore, the main challenge is to apply modeling methods to issues of efficiency and robustness. Thus, in the domain of animal nutrition, the recent systems of feeding units have proposed interesting advances that will soon be applicable in the field.

[1]  H. H. Laar,et al.  Milk yield and milk composition responses to change in predicted net energy and metabolizable protein: a meta-analysis. , 2016, Animal : an international journal of animal bioscience.

[2]  O. Martin,et al.  Robustesse, rusticité, flexibilité, plasticité... les nouveaux critères de qualité des animaux et des systèmes d'élevage: définitions systémique et biologique des différents concepts , 2010 .

[3]  J. H. M. Thornley,et al.  The lactation curve in cattle: a mathematical model of the mammary gland , 1983, The Journal of Agricultural Science.

[4]  J. Wilkinson Re-defining efficiency of feed use by livestock. , 2011, Animal : an international journal of animal bioscience.

[5]  W. Cannon The Wisdom of the Body , 1932 .

[6]  S. Sørensen,et al.  Changes in rumen bacterial and archaeal communities over the transition period in primiparous Holstein dairy cows. , 2018, Journal of dairy science.

[7]  Jan Dijkstra,et al.  Mathematical modelling and integration of rumen fermentation processes , 1993 .

[8]  J. Philipsson,et al.  Breeding for robustness in cattle. , 2009 .

[9]  K. Weigel,et al.  Use of genotype × environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle. , 2017, Journal of dairy science.

[10]  D. Sauvant,et al.  Ingestive behaviour of grazing ruminants: meta-analysis of the components of bite mass , 2019, Animal Feed Science and Technology.

[11]  T. Mary-Huard,et al.  Characterizing individual differences in animal responses to a nutritional challenge: Toward improved robustness measures. , 2016, Journal of dairy science.

[12]  D. Sauvant,et al.  Modelling homeostatic and homeorhetic regulations in lactating animals , 1994 .

[13]  Rafael Muñoz-Tamayo,et al.  Mechanistic modelling of in vitro fermentation and methane production by rumen microbiota , 2016 .

[14]  D E Bauman,et al.  Partitioning of nutrients during pregnancy and lactation: a review of mechanisms involving homeostasis and homeorhesis. , 1980, Journal of dairy science.

[15]  M. Tichit,et al.  An individual-based model simulating goat response variability and long-term herd performance. , 2010, Animal : an international journal of animal bioscience.

[16]  A. Offner,et al.  Comparative evaluation of the Molly, CNCPS, and LES rumen models , 2004 .

[17]  D. Sauvant,et al.  Dynamic model of the lactating dairy cow metabolism. , 2007, Animal : an international journal of animal bioscience.

[18]  T. Clutton‐Brock,et al.  Selection for foraging efficiency during a population crash in Soay sheep , 1995 .

[19]  R. L. Baldwin,et al.  Modeling ruminant digestion and metabolism. , 1999, Advances in experimental medicine and biology.

[20]  Roel F. Veerkamp,et al.  Genetic concepts to improve robustness of dairy cows , 2007 .

[21]  P. Faverdin,et al.  GrazeIn: a model of herbage intake and milk production for grazing dairy cows. 2. Prediction of intake under rotational and continuously stocked grazing management , 2011 .

[22]  M. Eugène,et al.  INRA feeding system for ruminants , 2018 .

[23]  N. C. Friggens,et al.  Des animaux plus robustes: un enjeu majeur pour le développement durable des productions animales nécessitant l'essor du phénotypage fin et à haut débit , 2014 .

[24]  D Sauvant,et al.  Modeling of off-feed periods caused by subacute acidosis in intensive lactating ruminants: application to goats. , 2009, Journal of dairy science.

[25]  P. Faverdin,et al.  Identification of biological traits associated with differences in residual energy intake among lactating Holstein cows. , 2018, Journal of dairy science.

[26]  P. Faverdin,et al.  Actualisation des besoins protéiques des ruminants et détermination des réponses des femelles laitières aux apports de protéines digestibles dans l’intestin , 2015 .

[27]  D. Sauvant,et al.  Approche quantitative de l'acidose chez les ruminants , 2015 .

[28]  D. Sauvant,et al.  Development of a mechanistic model of intake, chewing and digestion in cattle in connection with updated feed units , 2014 .

[29]  D. Sauvant,et al.  A mechanistic model of intake and grazing behaviour in sheep integrating sward architecture and animal decisions , 2003 .

[30]  Modeling homeorhetic trajectories of milk component yields, body composition and dry-matter intake in dairy cows: Influence of parity, milk production potential and breed. , 2017, Animal : an international journal of animal bioscience.

[31]  D. Sauvant,et al.  Quantification of the main digestive processes in ruminants: the equations involved in the renewed energy and protein feed evaluation systems. , 2016, Animal : an international journal of animal bioscience.

[32]  D. Sauvant,et al.  Development of a mechanistic model for rumen digestion validated using the duodenal flux of amino acids. , 1995, Reproduction, nutrition, development.

[33]  H. Makkar,et al.  Optimization of feed use efficiency in ruminant production systems. FAO Symposium Proceedings, Bangkok, Thailand, 27 November 2012. , 2013 .

[34]  François Bocquier,et al.  Adaptive abilities of the females and sustainability of ruminant livestock systems. A review , 2006 .

[35]  D. Sauvant,et al.  Modèle intégratif du tube digestif intégrant les interactions digestives, les flux de nutriments d’intérêt et compatible avec les systèmes UF et PDI , 2012 .

[36]  H. van Laar,et al.  A method to estimate cow potential and subsequent responses to energy and protein supply according to stage of lactation. , 2017, Journal of dairy science.

[37]  J. Dijkstra,et al.  Update of the Dutch protein evaluation system for ruminants: the DVE/OEB2010 system , 2010, The Journal of Agricultural Science.

[38]  D. Sauvant,et al.  Mechanistic model of intake of tropical pasture, depending on the growth and morphology of forage at a vegetative stage , 2014 .

[39]  Juan J. Villalba,et al.  Modelling Preference and Diet Selection Patterns by Grazing Ruminants: A Development in a Mechanistic Model of a Grazing Dairy Cow, MINDY , 2015 .

[40]  D Sauvant,et al.  A teleonomic model describing performance (body, milk and intake) during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. 2. Voluntary intake and energy partitioning. , 2010, Animal : an international journal of animal bioscience.