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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
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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

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