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Genetic and phenotypic parameters of lactations longer than 305 days (extended lactations)

Published online by Cambridge University Press:  01 March 2008

M. Haile-Mariam  and
M. E. Goddard
M. Haile-Mariam*
Affiliation:
Animal Genetics and Genomics, Department of Primary Industries, Attwood, Vic. 3049, Australia
M. E. Goddard
Affiliation:
Animal Genetics and Genomics, Department of Primary Industries, Attwood, Vic. 3049, Australia Faculty of Land and Food Resources, University of Melbourne, Parkville, Vic. 3052, Australia
*
E-mail: [email protected]
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Abstract

Test-day milk yield and somatic cell count data over extended lactation (lactation to 540–600 days) were analysed considering part lactations as different traits and fitting random regression (RR) models. Data on Australian Jersey and Holstein Friesian (HF) were used to demonstrate the shape of the lactation curve and data on HF were used for genetic study. Test-day data from about 100 000 cows that calved between 1998 and 2005 were used for this study. In all analyses, a sire model was used.

When part lactations were considered as different traits, protein yield early in the lactation (e.g. first 2 months) had a genetic correlation of about 0.8 with protein yield produced after 300 days of lactation. Genetic correlations between lactation stages that are adjacent to each other were high (0.9 or more) within parity. Across parities, genetic correlations were high for both protein and milk yield if they are within the same stage of lactation. Phenotypic correlations were lower than genetic correlations. Heritability of milk-yield traits estimated from the RR model varied from 0.15 at the beginning of the lactation to as high as 0.37 by the 4th month of lactation. All genetic correlations between different days in milk were positive, with the highest correlations between adjacent days in milk and decreasing correlations with increasing time-span. The pattern of genetic correlations between milk yield in the second 300 days (301 to 600 days of lactation) do not markedly differ from the pattern in the first 300 days of lactation. The lowest estimated genetic correlation was 0.15 between milk yield on days 45 and 525 of lactation. The result from this study shows that progeny of bulls with high estimated breeding values for yield traits and those that produce at a relatively high level in the first few months are the most likely candidates for use in herds favouring extended lactations.

Keywords

correlationdairy cowsmilk-yield traitslactationvariation
Type
Full Paper
Information
animal , Volume 2 , Issue 3 , March 2008 , pp. 325 - 335
Copyright
Copyright © The Animal Consortium 2008

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References

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
Australian Dairy Herd Improvement Scheme 2005. Australian dairy herd improvement report 2003/2004. ADHIS Pty Ltd, Melbourne, Australia.Google Scholar
Borman, JM, Macmillan, KL, Fahey, J 2004. The potential for extended lactations in Victorian dairying: a review. Australian Journal of Experimental Agriculture 44, 507–519.CrossRefGoogle Scholar
Bormann, J, Wiggans, GR, Druet, T, Gengler, N 2003. Within-herd effects of age at test day and lactation stage on test-day yields. Journal of Dairy Science 86, 3765–3774.CrossRefGoogle ScholarPubMed
Bertilsson, J, Berglund, B, Ratnayake, G, Svennersten-Sjaunja, K, Wiktorsson, H 1997. Optimising lactation cycles for the existing high-yielding dairy cow. A European perspective. Livestock Production Science 50, 5–13.CrossRefGoogle Scholar
Dekkers, JCM, Ten Hag, JH, Weersink, A 1998. Economic aspects of persistency of lactation in dairy cattle. Livestock Production Science 53, 237–252.CrossRefGoogle Scholar
Druet, T, Jaffrézic, F, Boichard, D, Ducrocq, V 2003. Modeling lactation curves and estimation of genetic parameters for first lactation test-day records of French Holstein cows. Journal of Dairy Science 86, 2480–2490.CrossRefGoogle ScholarPubMed
Farm Animal Welfare Council 1997. Report on the welfare of dairy cattle. FAWC, Ministry of Agriculture, Fisheries and Food, London, UK. Retrieved October 23, 2006, from http://www.fawc.org.uk/reports/dairycow/dcowr075.htmGoogle Scholar
Gilmour, AR, Gogel, BJ, Cullis, BR, Thompson, R 2006. ASReml User Guide Release 2.0. VSN International Ltd, Hemel Hempstead, UK.Google Scholar
Haile-Mariam, M, Bowman, PJ, Goddard, ME 2001. Estimates of genetic parameters for daily somatic cell count of Australian dairy cattle. Journal of Dairy Science 84, 1255–1264.CrossRefGoogle ScholarPubMed
Haile-Mariam, M, Bowman, PJ, Goddard, ME 2003. Genetic and environmental relationship among calving interval, survival rate, persistency of milk yield and somatic cell count in Dairy cattle. Livestock Production Science 80, 189–200.CrossRefGoogle 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
Meyer, K, Graser, H-U, Hammond, K 1989. Estimates of genetic parameters for first lactation test day production of Australian black and white cows. Livestock Production Science 21, 177–199.CrossRefGoogle Scholar
Olori, VE, Hill, WG, McGuirk, BJ, Brotherstone, S 1999. Estimating variance components for test day milk records by restricted maximum likelihood with a random regression animal model. Livestock Production Science 61, 53–63.CrossRefGoogle Scholar
Pool, MH, Janss, LLG, Meuwissen, THE 2000. Genetic parameters of Legendre polynomials for first parity lactation curves. Journal of Dairy Science 83, 2640–2649.CrossRefGoogle ScholarPubMed
Schaeffer, LR 2004. Application of random regression models in animal breeding. Livestock Production Science 86, 35–45.CrossRefGoogle Scholar
Schutz, MM, Hansen, LB, Steuernagel, GR, Kuck, AL 1990. Variation of milk, fat, protein, and somatic cells for dairy cattle. Journal of Dairy Science 73, 484–493.CrossRefGoogle Scholar
Schutz, MM, VanRaden, PM, Wiggans, GR 1994. Genetic variation in lactation means of somatic cell scores for six breeds of dairy cattle. Journal of Dairy Science 77, 284–293.CrossRefGoogle ScholarPubMed
Silvestre, AM, Petim-Batista, F, Colaço, J 2005. Genetic parameter estimates of Portuguese dairy cows for milk, fat, and protein using a spline test-day model. Journal of Dairy Science 88, 1225–1230.CrossRefGoogle ScholarPubMed
Swalve, HH 2000. Theoretical basis and computational methods for different test-day genetic evaluation methods. Journal of Dairy Science 83, 1115–1124.CrossRefGoogle ScholarPubMed
Van Amburgh, ME, Galton, DM, Bauman, DE, Everett, RW 1997. Management and economics of extended calving intervals with use of bovine somatotropin. Livestock Production Science 50, 15–28.CrossRefGoogle Scholar
Van der Werf, JHJ, Goddard, ME, Meyer, K 1998. The use of covariance functions and random regressions for genetic evaluation of milk production based on test day records. Journal of Dairy Science 81, 3300–3308.CrossRefGoogle ScholarPubMed
Veerkamp, RF, Goddard, ME 1998. Covariance functions across herd production levels for test day records on milk, fat, and protein yields. Journal of Dairy Science 81, 1690–1701.CrossRefGoogle ScholarPubMed
Wilmink, JBM 1988. Effects of incomplete records on genetic parameters among cumulative yields in first lactation and on extension of part lactations. Livestock Production Science 18, 19–34.CrossRefGoogle Scholar