Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Home
  • Home
Article

Modeling homeorhetic trajectories of milk component yields, body composition and dry-matter intake in dairy cows: Influence of parity, milk production potential and breed

  • J. B. Daniel (a1) (a2), N. C. Friggens (a1), H. van Laar (a2), K. L. Ingvartsen (a3) and D. Sauvant (a1)...

Abstract

The control of nutrient partitioning is complex and affected by many factors, among them physiological state and production potential. Therefore, the current model aims to provide for dairy cows a dynamic framework to predict a consistent set of reference performance patterns (milk component yields, body composition change, dry-matter intake) sensitive to physiological status across a range of milk production potentials (within and between breeds). Flows and partition of net energy toward maintenance, growth, gestation, body reserves and milk components are described in the model. The structure of the model is characterized by two sub-models, a regulating sub-model of homeorhetic control which sets dynamic partitioning rules along the lactation, and an operating sub-model that translates this into animal performance. The regulating sub-model describes lactation as the result of three driving forces: (1) use of previously acquired resources through mobilization, (2) acquisition of new resources with a priority of partition towards milk and (3) subsequent use of resources towards body reserves gain. The dynamics of these three driving forces were adjusted separately for fat (milk and body), protein (milk and body) and lactose (milk). Milk yield is predicted from lactose and protein yields with an empirical equation developed from literature data. The model predicts desired dry-matter intake as an outcome of net energy requirements for a given dietary net energy content. The parameters controlling milk component yields and body composition changes were calibrated using two data sets in which the diet was the same for all animals. Weekly data from Holstein dairy cows was used to calibrate the model within-breed across milk production potentials. A second data set was used to evaluate the model and to calibrate it for breed differences (Holstein, Danish Red and Jersey) on the mobilization/reconstitution of body composition and on the yield of individual milk components. These calibrations showed that the model framework was able to adequately simulate milk yield, milk component yields, body composition changes and dry-matter intake throughout lactation for primiparous and multiparous cows differing in their production level.

Copyright

© The Animal Consortium 2017 

Corresponding author

† E-mail: jean-baptiste.daniel@trouwnutrition.com

References

Hide All
Agricultural and Food Research Council 1993. Energy and protein requirements of ruminants. An advisory manual prepared by the AFRC Technical Committee on response to Nutrients. CAB International, Wallingford, UK.
Baldwin, RL, France, J, Beever, DE, Gill, M and Thornley, JHM 1987. Metabolism of the lactating cow: III. Properties of mechanistic models suitable for evaluation of energetic relationships and factors involved in the partition of nutrients. Journal of Dairy Research 54, 133–145.
Baudracco, J, Lopez-Villalobos, N, Holmes, CW, Comeron, EA, Macdonald, KA, Barry, TN and Friggens, NC 2012. e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding. Animal 6, 980–993.
Bauman, DE and Currie, WB 1980. Partitioning of nutrients during pregnancy and lactation: a review of mechanisms involving homeostasis and homeorhesis. Journal of Dairy Science 63, 1514–1529.
Bell, AW, Slepetis, R and Ehrhardt, RA 1995. Growth and accretion of energy and protein in the gravid uterus during late pregnancy in Holstein cows. Journal of Dairy Science 78, 1954–1961.
Berry, DP, Buckley, F, Dillon, P, Evans, RD, Rath, M and Veerkamp, RF 2003. Genetic relationships among body condition score, body weight, milk yield, and fertility in dairy cows. Journal of Dairy Science 86, 2193–2204.
Bibby, J and Toutenburg, H 1977. Prediction and improved estimation in linear models. Wiley, Berlin, Germany.
Birnie, JW, Agnew, RE and Gordon, FJ 2000. The influence of body condition on the fasting energy metabolism of nonpregnant, nonlactating dairy cows. Journal of Dairy Science 83, 1–7.
Brun-Lafleur, L, Delaby, L, Husson, F and Faverdin, P 2010. Predicting energy×protein interaction on milk yield and milk composition in dairy cows. Journal of Dairy Science 93, 4128–4143.
Cherwell Scientific Ltd 2000. Modelmaker user manual. Cherwell Scientific Ltd, Oxford, England.
Coffey, MP, Simm, G and Brotherstone, S 2002. Energy balance profiles for the first three lactations of dairy cows estimated using random regression models. Journal of Dairy Science 85, 2669–2678.
Coffey, MP, Simm, G, Oldham, JD, Hill, WG and Brotherstone, S 2004. Genotype and diet effects on energy balance in the first three lactations of dairy cows. Journal of Dairy Science 87, 4318–4326.
Danfaer, A 1990. A dynamic model of nutrient digestion and metabolism in lactating dairy cows. Ph.D. thesis. National Institute of Animal Science, Foulum, Denmark.
Daniel, JB, Friggens, NC, Chapoutot, P, Van Laar, H and Sauvant, D 2016. Milk yield and milk composition responses to change in predicted net energy and metabolizable protein: a meta-analysis. Animal 10, 1975–1985.
Daniel, JB, Friggens, NC, Van Laar, H, Ferris, CP and Sauvant, D 2017. A method to estimate cow potential and subsequent responses to energy and protein supply according to stage of lactation. Journal of Dairy Science 100, 3641–3657.
Dijkstra, J, France, J, Dhanoa, MS, Maas, JA, Hanigan, MD, Rook, AJ and Beever, DE 1997. A model to describe growth patterns of the mammary gland during pregnancy and lactation. Journal of Dairy Science 80, 2340–2354.
Emmans, GC and Fisher, C 1986. Problems in nutritional theory. In Nutrient requirements of poultry and nutritional research (ed. C Fisher and KN Boorman), pp. 9–39. Butterworths, London.
Faverdin, P, Delagarde, R, Delaby, L and Meschy, F 2010. Alimentation des vaches laitières. In Alimentation des bovins, ovins et caprins. Besoins des animaux – Valeur des aliments – Tables INRA 2007, mise à jour 2010, pp. 23–58. Editions Quae, Versailles, France.
Faverdin, P, Hoden, A and Coulon, JB 1987. Recommandations alimentaires pour les vaches laitières. INRA, Bulletin Technique CRZV Theix 70, 133–152.
Ferrell, CL, Garrett, WN, Hinman, N and Grichting, G 1976. Energy utilization by pregnant and non-pregnant heifers. Journal of Animal Science 42, 937–950.
Fox, DG, Tedeschi, LO, Tylutki, TP, Russell, JB, Van Amburgh, ME, Chase, LE, Pell, AN and Overton, TR 2004. The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion. Animal Feed Science and Technology 112, 29–78.
Friggens, NC 2003. Body lipid reserves and the reproductive cycle: towards a better understanding. Livestock Production Science 83, 219–236.
Friggens, NC, Brun-Lafleur, L, Faverdin, P, Sauvant, D and Martin, O 2013. Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time. Animal 7, 89–101.
Friggens, NC, Ingvartsen, KL and Emmans, GC 2004. Prediction of body lipid change in pregnancy and lactation. Journal of Dairy Science 87, 988–1000.
Jacquot, AL, Delaby, L, Pomiés, D, Brunschwig, G and Baumont R, R. 2015. Dynamic model of milk production responses to grass-based diet variations during grazing and indoor housing. Journal of Agricultural Science 153, 689–707.
Johnson, IR, France, J and Cullen, BR 2016. A model of milk production in lactating dairy cows in relation to energy and nitrogen dynamics. Journal of Dairy Science 99, 1605–1618.
Leclerc, H 2008. Development of the French dairy cattle test-day model genetic evaluation and prospects of using results for herd management. Ph.D. thesis. AgroParisTech, Paris, France.
Lin, LIK 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268.
Loker, S, Bastin, C, Miglior, F, Sewalem, A, Schaeffer, LR, Jamrozik, J, Ali, A and Osborne, V 2012. Genetic and environmental relationships between body condition score and milk production traits in Canadian Holsteins. Journal of Dairy Science 95, 410–419.
Martin, O and Sauvant, D 2007. Dynamic model of the lactating dairy cow metabolism. Animal 1, 1143–1166.
Martin, O and Sauvant, D 2010. A teleonomic model describing performance (body, milk and intake) during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. 1. Trajectories of life function priorities and genetic scaling. Animal 4, 2030–2047.
Miglior, F, Sewalem, A, Jamrozik, J, Bohmanova, J, Lefebvre, DM and Moore, RK 2007. Genetic analysis of milk urea nitrogen and lactose and their relationships with other production traits in Canadian Holstein cattle. Journal of Dairy Science 90, 2468–2479.
Neal, HDSC and Thornley, JHM 1983. The lactation curve in cattle: a mathematical model of the mammary gland. Journal of Agricultural Science 101, 389–400.
Nielsen, HM, Friggens, NC, Lovendahl, P, Jensen, J and Ingvartsen, KL 2003. Influence of breed, parity, and stage of lactation on lactational performance and relationship between body fatness and live weight. Livestock Production Science 79, 119–133.
National Research Council 2001. Nutrient requirements of dairy cattle, 7th revised edition. National Academy Press, Washington, DC, USA.
Puillet, L, Martin, O, Tichit, M and Sauvant, D 2008. Simple representation of physiological regulations in a model of lactating female: application to the dairy goat. Animal 2, 235–246.
Ruelle, E, Delaby, L, Wallace, M and Shalloo, L 2016. Development and evaluation of the herd dynamic milk model with focus on the individual cow component. Animal 10, 1986–1997.
Sauvant, D 1994. Modelling homeostatic and homeorhetic regulations in lactating animals. Livestock Production Science 39, 105–113.
Sauvant, D, Ortigues-Marty, I, Giger-Reverdin, S and Nozière, P 2015. Updating energy requirements and efficiency of dairy ruminant females. Rencontre Recherche Ruminants 22, 225–228.
Spurlock, DM, Dekkers, JCM, Fernando, R, Koltes, DA and Wolc, A 2012. Genetic parameters for energy balance, feed efficiency, and related traits in Holstein cattle. Journal of Dairy Science 95, 5393–5402.
Veerkamp, RF and Thompson, R 1999. A covariance function for feed intake, live weight, and milk yield estimated using a random regression model. Journal of Dairy Science 82, 1565–1573.
Volden, H 2011. NorFor – The Nordic feed evaluation system. EAAP Publications No 130. Wageningen Academic Publishers, Wageningen, The Netherlands.
Wood, PDP 1967. Algebraic model of the lactation curve in cattle. Nature 216, 164–165.
Zom, R 2014. The development of a model for the prediction of feed intake and energy partitioning in dairy cows. Ph.D. thesis. Wageningen University, Wageningen, The Netherlands.

Keywords

Type Description Title
WORD
Supplementary materials

Daniel et al supplementary material
Daniel et al supplementary material 1

 Word (287 KB)
287 KB

Modeling homeorhetic trajectories of milk component yields, body composition and dry-matter intake in dairy cows: Influence of parity, milk production potential and breed

  • J. B. Daniel (a1) (a2), N. C. Friggens (a1), H. van Laar (a2), K. L. Ingvartsen (a3) and D. Sauvant (a1)...

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed