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

Published online by Cambridge University Press:  03 November 2017

J. B. Daniel ,
N. C. Friggens ,
H. van Laar ,
K. L. Ingvartsen  and
D. Sauvant
J. B. Daniel*
Affiliation:
UMR 0791 Modélisation Systémique Appliquée aux Ruminants, INRA, AgroParisTech, Université Paris-Saclay, 75005 Paris, France Trouw Nutrition R&D, PO Box 220, 5830 AE Boxmeer, The Netherlands
N. C. Friggens
Affiliation:
UMR 0791 Modélisation Systémique Appliquée aux Ruminants, INRA, AgroParisTech, Université Paris-Saclay, 75005 Paris, France
H. van Laar
Affiliation:
Trouw Nutrition R&D, PO Box 220, 5830 AE Boxmeer, The Netherlands
K. L. Ingvartsen
Affiliation:
Faculty of Agricultural Sciences, Research Center Foulum, University of Aarhus, PO Box 50, DK-8830 Tjele, Denmark
D. Sauvant
Affiliation:
UMR 0791 Modélisation Systémique Appliquée aux Ruminants, INRA, AgroParisTech, Université Paris-Saclay, 75005 Paris, France
*
†E-mail: [email protected]
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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.

Keywords

dairy cowmilk compositiondynamic modelenergy
Type
Research Article
Information
animal , Volume 12 , Issue 6 , June 2018 , pp. 1182 - 1195
Copyright
© The Animal Consortium 2017 

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References

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.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Bibby, J and Toutenburg, H 1977. Prediction and improved estimation in linear models. Wiley, Berlin, Germany.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
Cherwell Scientific Ltd 2000. Modelmaker user manual. Cherwell Scientific Ltd, Oxford, England.Google Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Faverdin, P, Hoden, A and Coulon, JB 1987. Recommandations alimentaires pour les vaches laitières. INRA, Bulletin Technique CRZV Theix 70, 133–152.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
Friggens, NC 2003. Body lipid reserves and the reproductive cycle: towards a better understanding. Livestock Production Science 83, 219–236.Google Scholar
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.Google Scholar
Friggens, NC, Ingvartsen, KL and Emmans, GC 2004. Prediction of body lipid change in pregnancy and lactation. Journal of Dairy Science 87, 988–1000.CrossRefGoogle ScholarPubMed
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.Google Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
Lin, LIK 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268.Google Scholar
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.CrossRefGoogle ScholarPubMed
Martin, O and Sauvant, D 2007. Dynamic model of the lactating dairy cow metabolism. Animal 1, 1143–1166.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
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.Google Scholar
National Research Council 2001. Nutrient requirements of dairy cattle, 7th revised edition. National Academy Press, Washington, DC, USA.Google Scholar
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.Google Scholar
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.Google Scholar
Sauvant, D 1994. Modelling homeostatic and homeorhetic regulations in lactating animals. Livestock Production Science 39, 105–113.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Volden, H 2011. NorFor – The Nordic feed evaluation system. EAAP Publications No 130. Wageningen Academic Publishers, Wageningen, The Netherlands.Google Scholar
Wood, PDP 1967. Algebraic model of the lactation curve in cattle. Nature 216, 164–165.Google Scholar
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.Google Scholar
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