Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-23T00:42:46.376Z Has data issue: false hasContentIssue false
  • English
  • Français

Analysis of lactation shapes in extended lactations

Published online by Cambridge University Press:  03 April 2012

R. Steri ,
C. Dimauro ,
F. Canavesi ,
E. L. Nicolazzi  and
N. P. P. Macciotta
R. Steri*
Affiliation:
Dipartimento di Scienze Zootecniche, Università di Sassari, Via Enrico De Nicola 9, 07100 Sassari, Italy
C. Dimauro
Affiliation:
Dipartimento di Scienze Zootecniche, Università di Sassari, Via Enrico De Nicola 9, 07100 Sassari, Italy
F. Canavesi
Affiliation:
Associazione Nazionale Allevatori Frisona Italiana, Via Bergamo 292, 26100 Cremona, Italy
E. L. Nicolazzi
Affiliation:
Associazione Nazionale Allevatori Frisona Italiana, Via Bergamo 292, 26100 Cremona, Italy Istituto di Zootecnica, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
N. P. P. Macciotta
Affiliation:
Dipartimento di Scienze Zootecniche, Università di Sassari, Via Enrico De Nicola 9, 07100 Sassari, Italy
*
†E-mail: [email protected]
Get access

Abstract

In order to describe the temporal evolution of milk yield (MY) and composition in extended lactations, 21 658 lactations of Italian Holstein cows were analyzed. Six empirical mathematical models currently used to fit 305 standard lactations (Wood, Wilmink, Legendre, Ali and Schaeffer, quadratic and cubic splines) and one function developed specifically for extended lactations (a modification of the Dijkstra model) were tested to identify a suitable function for describing patterns until 1000 days in milk (DIM). Comparison was performed on individual patterns and on average curves grouped according to parity (primiparous and multiparous) and lactation length (standard ⩽305 days, and extended from 600 to 1000 days). For average patterns, polynomial models showed better fitting performances when compared with the three or four parameters models. However, LEG and spline regression, showed poor prediction ability at the extremes of the lactation trajectory. The Ali and Schaeffer polynomial and Dijkstra function were effective in modelling average curves for MY and protein percentage, whereas a reduced fitting ability was observed for fat percentage and somatic cell score. When individual patterns were fitted, polynomial models outperformed nonlinear functions. No detectable differences were observed between standard and extended patterns in the initial phase of lactation, with similar values of peak production and time at peak. A considerable difference in persistency was observed between 200 and 305 DIM. Such a difference resulted in an estimated difference between standard and extended cycle of about 7 and 9 kg/day for daily yield at 305 DIM and of 463 and 677 kg of cumulated milk production at 305 DIM for the first- and second-parity groups, respectively. For first and later lactation animals, peak yield estimates were nearly 31 and 38 kg, respectively, and occurred at around 65 and 40 days. The asymptotic level of production was around 9 kg for multiparous cows, whereas the estimate was negative for first parity.

Keywords

dairy cattleextended lactationmathematical model
Type
Full Paper
Information
animal , Volume 6 , Issue 10 , October 2012 , pp. 1572 - 1582
Copyright
Copyright © The Animal Consortium 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ali, TE, Schaeffer, LR 1987. Accounting for covariances among test day milk yields in dairy cows. Canadian Journal of Animal Science 67, 637–644.CrossRefGoogle Scholar
Auldist, MJ, O'Brien, G, Cole, D, Macmillan, KL, Grainger, C 2007. Effects of varying lactation length on milk production capacity of cows in pasture-based dairying systems. Journal of Dairy Science 90, 3234–3241.CrossRefGoogle ScholarPubMed
Bertilsson, J, Svennersten-Sjaunja, K, Wiktorsson, H, Berglund, B, Ratnayake, G 1997. Optimising lactation cycles for the existing high-yielding dairy cow. A European perspective. Livestock Production Science 50, 5–13.CrossRefGoogle Scholar
Brotherstone, S, Thompsonb, R, Whitea, IMS 2004. Effects of pregnancy on daily milk yield of Holstein–Friesian dairy cattle. Livestock Production Science 87, 265–269.CrossRefGoogle Scholar
Butler, ST, Shalloo, L, Murphy, JJ 2010. Extended lactations in a seasonal-calving pastoral system of production to modulate the effects of reproductive failure. Journal of Dairy Science 93, 1283–1295.CrossRefGoogle Scholar
Cole, JB, Null, DJ 2009. Genetic evaluation of lactation persistency for five breeds of dairy cattle. Journal of Dairy Science 92, 2248–2258.CrossRefGoogle ScholarPubMed
Dematawewa, CMB, Pearson, RE, VanRaden, PM 2006. Modeling extended lactations in Holsteins. Journal of Dairy Science 89 (Suppl. 1), 96–97.Google Scholar
Dematawewa, CMB, Pearson, RE, VanRaden, PM 2007. Modeling extended lactations of Holsteins. Journal of Dairy Science 90, 3924–3936.CrossRefGoogle ScholarPubMed
Dijkstra, J, France, J, Dhanoa, MS, Maas, JA, Hanigan, MD, Rook, AJ, Beever, DE 1997. A model to describe growth patterns of the mammary gland during pregnancy and lactation. Journal of Dairy Science 80, 2340–2354.CrossRefGoogle Scholar
Dobson, H, Smith, RF, Royal, MD, Knight, CH, Sheldon, IM 2007. The high-producing dairy cow and its reproductive performance. Reproduction in Domestic Animals 42, 17–23.CrossRefGoogle ScholarPubMed
Erb, HN, Smith, RD, Oltenacu, PA, Guard, CL, Hillman, RB, Powers, PA, Smith, MC, White, ME 1984. Path model of reproductive disorders and performance, milk fever, mastitis, milk yield, and culling in Holstein cows. Journal of Dairy Science 68, 3337–3349.CrossRefGoogle Scholar
Gonzalez-Recio, O, Perez-Cabal, MA, Alenda, R 2004. Economic value of female fertility and its relationship with profit in Spanish dairy cattle. Journal of Dairy Science 87, 3053–3061.CrossRefGoogle ScholarPubMed
Grossman, M, Koops, WJ 2003. Modeling extended lactation curves of dairy cattle: a biological basis for the multiphasic approach. Journal of Dairy Science 86, 988–998.CrossRefGoogle ScholarPubMed
Haile-Mariam, M, Goddard, M 2008. Genetic and phenotypic parameters of lactations longer than 305 days (extended lactations). Animal 2, 325–335.CrossRefGoogle ScholarPubMed
Knight, H 2005. Extended lactation: turning theory into reality. Advances in Dairy Technology 17, 113–124.Google Scholar
Lucy, MC 2001. Reproductive loss in high-producing dairy cattle: Where will it end? Journal of Dairy Science 84, 1277–1293.CrossRefGoogle ScholarPubMed
Macciotta, NPP, Vicario, D, Cappio-Borlino, A 2005. Detection of different shapes of lactation curve for milk yield in dairy cattle by empirical mathematical models. Journal of Dairy Science 88, 1178–1191.CrossRefGoogle ScholarPubMed
Macciotta, NPP, Miglior, F, Dimauro, C, Schaeffer, LR 2010. Comparison of parametric, orthogonal, and spline functions to model individual lactation curves for milk yield in Canadian Holsteins. Italian Journal of Animal Science 9, 460–464.CrossRefGoogle Scholar
Muir, BL, Kistemaker, G, Van Doormaal, BJ 2004. Estimation of genetic parameters for the Canadian Test Day Model with Legendre polynomials for Holsteins based on more recent data. Report to the Dairy Cattle Breeding and Genetics Committee and the Genetic Evaluation Board, March 2004. Retrieved September 8, 2011, from http://cgil.uoguelph.ca/ Google Scholar
Muir, BL, Kistemaker, G, Jamrozik, J, Canavesi, F 2007. Genetic parameters for a multiple-trait multiple-lactation random regression test-day model in Italian Holsteins. Journal of Dairy Science 90, 1564–1574.CrossRefGoogle ScholarPubMed
Olori, VE, Brotherstone, S, Hill, WG, McGuirk, BJ 1999. Fit of standard models of the lactation curve to weekly records of milk production of cows in a single herd. Livestock Production Science 58, 55–63.CrossRefGoogle Scholar
Reinhardt, F, Liu, Z, Bünger, A, Dopp, L, Reents, R 2002. Impact of application of a random regression test-day model to production trait genetic evaluations in dairy cattle. In Proceedings of the Interbull Meeting 27, Interlaken, Switzerland, pp. 103–108.Google Scholar
Rook, AJ, France, J, Dhanoa, MS 1993. On the mathematical description of lactation curves. Journal of Agricultural Science 121, 97–102.CrossRefGoogle Scholar
Schaeffer, LR 2004. Application of random regression models in animal breeding. Livestock Production Science 86, 35–45.CrossRefGoogle Scholar
Schaeffer, LR, Jamrozik, J, Kistemaker, GJ, Van Doormaal, BJ 2000. Experience with a test day model. Journal of Dairy Science 83, 1135–1144.CrossRefGoogle ScholarPubMed
Silvestre, AM, Petim-Batista, F, Colaco, J 2006. The accuracy of seven mathematical functions in modeling dairy cattle lactation curves based on test-day records from varying sampling schemes. Journal of Dairy Science 89, 1813–1821.CrossRefGoogle Scholar
Stanton, TL, Jones, LR, Everett, RW, Kachman, SD 1992. Estimating milk, fat, and protein lactation curves with a test day model. Journal of Dairy Science 75, 1691–1700.CrossRefGoogle ScholarPubMed
VanRaden, PM, Dematawewa, CMB, Pearson, RE, Tooker, ME 2006. Productive life including all lactations and longer lactations with diminishing credits. Journal of Dairy Science 89, 3213–3220.CrossRefGoogle ScholarPubMed
Vargas, B, Koops, WJ, Herrero, M, van Arendonk, JAM 2000. Modeling extended lactations of dairy cows. Journal of Dairy Science 83, 1371–1380.CrossRefGoogle ScholarPubMed
Veerkamp, RF, Emmans, GC, Cromie, AR, Simm, G 1995. Variance components for residual feed intake in dairy cows. Livestock Production Science 41, 111–120.CrossRefGoogle Scholar
Wilmink, JBM 1987. Adjustment of test-day milk, fat and protein yield for age, season and stage of lactation. Livestock Production Science 16, 335–348.CrossRefGoogle Scholar
Wood, PDP 1967. Algebraic models of the lactation curves in cattle. Nature 216, 164–165.CrossRefGoogle Scholar
Wood, PDP 1968. Factors affecting persistency of lactation in cattle. Nature 218, 894.CrossRefGoogle Scholar