Hostname: page-component-848d4c4894-jbqgn Total loading time: 0 Render date: 2024-06-22T00:55:11.987Z Has data issue: false hasContentIssue false
  • English
  • Français

Modelling of lactation curves of dairy cows based on monthly test day milk yield records under inconsistent milk recording scenarios

Published online by Cambridge University Press:  22 June 2011

C. B. Wasike ,
A. K. Kahi  and
K. J. Peters
C. B. Wasike*
Affiliation:
Department of Agricultural Sciences, Pwani University College, P.O. Box 95, 80108 Kilifi, Kenya Institute of Animal Breeding in the Tropics and Subtropics, Humboldt University of Berlin, Philippstraße 13, Haus 9, 10115 Berlin, Germany
A. K. Kahi
Affiliation:
Animal Breeding and Genetics Group, Department of Animal Sciences, Egerton University, P.O. Box 536, 20115 Egerton, Kenya
K. J. Peters
Affiliation:
Institute of Animal Breeding in the Tropics and Subtropics, Humboldt University of Berlin, Philippstraße 13, Haus 9, 10115 Berlin, Germany
*
†E-mail: wasikebwire@yahoo.co.uk
Get access

Abstract

The objective of this study was to describe the lactation curve of dairy cattle in Kenya using a suitable lactation function in order to facilitate inclusion of partial lactations in national dairy cattle evaluation and to assess the effect of data characteristics on lactation curve parameters. Six functions were fitted to test day (TD) milk yield records from six parities of Ayrshire, Guernsey, Holstein Friesian, Jersey and Sahiwal cattle. Five datasets: DS-1 (12-TD dataset with randomly missing records), DS-2 (10-TD dataset without missing records), DS-3 (10-TD dataset with randomly missing records), DS-4 (7-TD dataset, with only TD 4 to 10 records) and DS-5 (7-TD dataset, with TD 1 to 4, 6, 8 and 10 records) depicting various recording circumstances were derived to assess the effects of data characteristics on lactation curves and to assess the feasibility of reducing the number of TD samples per lactation. The fit of the functions was evaluated using adjusted R2 and their predictive abilities were compared using mean square prediction error, percentage of squared bias and the correlation between the predicted and actual milk yield. These criteria plus the changes in the parameters of curve functions and their associated standard errors were used in determining the effects of data characteristics on lactation curves. The mechanistic functions of Dijkstra (DIJ) and Pollott (APOL), and the incomplete gamma function of Wood (WD) had the highest adjusted R2 > 0.75. The APOL function was eliminated due to convergence failures when analysis of individual lactations within breeds was carried out. Both DIJ and WD had good predictive ability, although DIJ performed slightly better. Convergence difficulties were noted in some DIJ analysis where data were limiting. Missing records, especially at the beginning of a lactation, greatly influenced parameters a and b of the functions. It also resulted in estimates with large standard errors. Missing records in later lactation hardly affected the parameter estimates. The WD and DIJ functions showed superior fit to the data. The WD function demonstrated higher adaptability to various data characteristics than DIJ and could be used in situations where animal recording is not consistently practised and where recording of animal performance is routinely practised. DIJ function had high data requirements, which restricts it to dairy systems with consistent recording, despite easy physiological interpretation of its parameters. The number of TD per lactation could be reduced by minimising sampling frequency in the later lactation while maintaining the monthly sampling frequency in early lactation.

Keywords

lactation curvedairy cattlelactation functiontest day milk yield
Type
Full Paper
Information
animal , Volume 5 , Issue 11 , 26 September 2011 , pp. 1780 - 1790
Copyright
Copyright © The Animal Consortium 2011

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 covariance among test day milk yields in dairy cows. Canadian Journal of Animal Science 67, 637–644.Google Scholar
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.Google Scholar
Food and Agricultural Organization (FAO) 1998. Secondary guidelines for development of national farm animal genetic resources management plans: animal recording for medium input production environment. Food and Agricultural Organization of the United Nations (FAO), Rome, Italy.Google Scholar
Guo, Z, Swalve, HH 1995. Modelling of lactation curve as a sub-model in the evaluation of test day records. Proceedings of the INTERBULL Meeting, INTERBULL Bulletin No. 11, Department of Animal Breeding and Genetics, SLU, Uppsala, Sweden, p. 4.Google Scholar
International Committee on Animal Recording (ICAR) 2009. International agreement of recording practices: guidelines approved by the general assembly held in Niagara Falls, USA, 18 June 2008. ICAR, Rome.Google Scholar
Jamrozik, J, Schaeffer, LR, Dekkers, JCM 1997. Genetic evaluation of dairy cattle using test day yields and random regression model. Journal of Dairy Science 80, 1217–1226.CrossRefGoogle ScholarPubMed
Jorjani, H, Philipsson, J, Mocquot, JC 2001. Interbull guidelines for national and international genetic evaluation systems in dairy cattle with focus on productive traits. INTERBULL Bulletin 28, SLU, Uppsala, Sweden.Google Scholar
Karanja, FK 2006. CROPWAT model analysis of water use in six districts in Kenya. CEEPA Discussion Paper No. 35, CEEPA, University of Pretoria. Retrieved February 16, 2010 from http://www.ceepa.co.za/docs/CDPNo35.pdf.Google Scholar
Lynch, M, Walsh, B 1998. Genetics and analysis of quantitative traits. Sinauer Associates Inc., Sunderland, USA.Google Scholar
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
Mrode, RA 2005. Linear models for the prediction of animal breeding values. CABI Publishing, Wallingford, UK.Google Scholar
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
Peeler, EJ, Omore, AO 1997. Manual of livestock production systems in Kenya – cattle sheep and goat systems. KARI/DFID National Agricultural Research Project II. National Veterinary Research Centre, Kikuyu, Kenya.Google Scholar
Pollott, GE 2000. A biological approach to lactation curve analysis for milk yield. Journal of Dairy Science 83, 2448–2458.CrossRefGoogle ScholarPubMed
Quinn, N, Killen, L, Buckley, F 2005. Empirical algebraic modelling of lactation curves using Irish data. Irish Journal of Agricultural and Food Research 44, 1–13.Google Scholar
SAS 2004. Statistical analysis systems user's guide, (release 9.1). SAS Institute Inc., Cary, NC, USA.Google Scholar
Sherchand, L, McNew, RW, Kellogg, DW, Johnson, B 1995. Selection of a mathematical model to generate lactation curves using daily milk yields of Holstein cows. Journal of Dairy Science 78, 2507–2513.CrossRefGoogle ScholarPubMed
Silvestre, AM, Petim-Batista, F, Colaço, J 2006. The accuracy of seven mathematical functions in modeling dairy cattle lactation curves based on test-day records from varying sample schemes. Journal of Dairy Science 89, 1813–1821.CrossRefGoogle ScholarPubMed
Silvestre, AMD, Petim-Batista, F, Colaço, J 2005. Genetic parameter estimates for milk, fat and protein using a spline test-day model. Journal of Dairy Science 88, 1225–1230.Google Scholar
Val-Arreola, D, Kebreab, E, Dijkstra, J, France, J 2004. Study of the lactation curve in dairy cattle on farms in Central Mexico. Journal of Dairy Science 87, 3789–3799.Google 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 model of the lactation curve in cattle. Nature 216, 164–165.CrossRefGoogle Scholar