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Evaluation of different approaches for the estimation of daily yield from single milk testing scheme in cattle

Published online by Cambridge University Press:  24 December 2009

Janez Jenko ,
Tomaž Perpar ,
Gregor Gorjanc  and
Drago Babnik
Janez Jenko*
Affiliation:
Agricultural Institute of Slovenia, Hacquetova 17, 1000Ljubljana, Slovenia
Tomaž Perpar
Affiliation:
Agricultural Institute of Slovenia, Hacquetova 17, 1000Ljubljana, Slovenia
Gregor Gorjanc
Affiliation:
University of Ljubljana, Biotechnical Faculty, Department of Animal Science, Groblje 3, 1230Domžale, Slovenia
Drago Babnik
Affiliation:
Agricultural Institute of Slovenia, Hacquetova 17, 1000Ljubljana, Slovenia
*
*For correspondence; e-mail: janez.jenko@kis.si
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Abstract

Three models for the estimation of milk, fat and protein daily yield (DY) based on a.m. (AM) or p.m. (PM) milkings were compared. A total of 518 766 test-day records from 5078 dairy cattle farms obtained between March 2004 and April 2008 were analysed. The DY model was a linear model with DY as a dependent variable. In the PYR model and the DYR model, partial yield ratios (AM:DY and PM:DY) and daily yield ratios (DY:AM and DY:PM), respectively, were used as a dependent variable in the first step. In the second step, DY was estimated as a partial yield divided (PYR model) or multiplied (DYR model) by the estimated yield ratio from the first step. Models included the effect of partial yield (only in the DY model), milking interval, stage (month) of lactation and parity. Analysis of variance indicated that partial yield was the most important source of variation for the DY model whereas milking interval had the biggest effect in the PYR model and the DYR model. Differences in accuracy (correlation between the true and the estimated DY) between the models were negligible. On the other hand, models differed in the amount of bias (average error). The DYR model on average overestimated DY by 0·13 kg, 0·01 kg and 0·01 kg for milk, fat and protein, respectively. For the other two models the overall bias was almost zero. However, the DY model overestimated low and underestimated high DY owing to the well known regression property. The DYR model progressively overestimated high DY. These problems were not observed with the PYR model which seemed to be the best model. In this paper a relatively old topic was analysed and discussed from a new point of view, where the estimation of DY is based on modelling biologically more stable partial yield ratios rather than yield values from a.m. or p.m. milking.

Keywords

Alternate a.m. – p.m. testing schemedaily yieldmilkfatproteincattle
Type
Research Article
Information
Journal of Dairy Research , Volume 77 , Issue 2 , May 2010 , pp. 137 - 143
Copyright
Copyright © Proprietors of Journal of Dairy Research 2009

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References

Cassandro, M, Carnier, P, Gallo, L, Mantovani, R, Contiero, B, Bittante, G & Jansen, GB 1995 Bias and accuracy of single milking testing schemes to estimate daily and lactation milk yield. Journal of Dairy Science 78 2884–2893CrossRefGoogle ScholarPubMed
Delorenzo, MA & Wiggans, GR 1986 Factors for estimating daily yield of milk, fat, and protein from a single milking for herds milked twice a day. Journal of Dairy Science 69 2386–2394CrossRefGoogle Scholar
Everett, RW & Wadell, LH 1970a Relationship between milking intervals and individual milk weights. Journal of Dairy Science 53 548–553CrossRefGoogle Scholar
Everett, RW & Wadell, LH 1970b Sources of variation affecting the difference between morning and evening daily milk production. Journal of Dairy Science 53 1424–1429CrossRefGoogle Scholar
Everett, RW & Wadell, LH 1970c Sources of variation affecting ratio factors for estimating total daily milk yield from individual milkings. Journal of Dairy Science 53 1430–1435CrossRefGoogle Scholar
Galton, F 1886 Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute 15 246–263Google Scholar
ICAR 2006 International Committee for Animal Recording: International Agreement of Recording Practices – Guidelines approved by the General Assembly, Kuopio. Finland 9 June 2006 475 pp.Google Scholar
Klopčič, M, Malovrh, Š, Gorjanc, G, Kovač, M & Osterc, J 2001 Model development for prediction of daily milk yield at alternating (AT) recording scheme. In Research Reports Suppl. 31 (Ed Stekar, J) pp 293–300Biotechnical faculty, Agriculture, University of Ljubljana, SloveniaGoogle Scholar
Klopčič, M, Malovrh, Š, Gorjanc, G & Kovač, M 2004 Prediction of daily milk fat and protein content using alternating (AT) recording scheme. Czech Journal of Animal Science 48 449–458Google Scholar
Liu, Z, Reents, R, Reinhardt, F & Kuwan, K 2000 Approaches to estimating daily yield from single milk testing schemes and use of a.m.-p.m. records in test-day model genetic evaluation in dairy cattle. Journal of Dairy Science 83 2672–2682CrossRefGoogle ScholarPubMed
Logar, B, Podogoršek, P, Jeretina, J, Ivanovič, B & Perpar, T 2005 Online avaliable milk-recording data for efficient support of farm management. In Knowledge Transfer in Cattle Husbandry (New Management Practices, Attitudes and Adaptation) (Eds Kuipers, A, Klopčič, M & Cled, T). Vol. 117, pp. 227–230Wageningen, the Netherlands: Wageningen Academic PublishersGoogle Scholar
Sadar, M, Logar, B, Perpar, T, Podgoršek, P, Žabjek, A & Ivanovič, B 2008 [Results of animal recording, Slovenia 2007]. Govedorejska služba Slovenije, Kmetijski inštitut Slovenije, Ljubljana66 pp.Google Scholar
Sadar, M, Podgoršek, P, Perpar, T, Logar, B, Ivanovič, B, Jeretina, J & Prevolnik, M 2005 [Results of animal recording, Slovenia 2004]. Govedorejska služba Slovenije, Kmetijski inštitut Slovenije, Ljubljana50 pp.Google Scholar
Smithson, M & Verkuilen, J 2006 A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychological Methods 11 54–71CrossRefGoogle ScholarPubMed
SAS 2002 SAS/STAT User's Guide (Version 8.2). Statistical Analysis System Inst., Cary NC, USAGoogle Scholar
R Development Core Team 2009 R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.orgGoogle Scholar