Using lactation curves as a tool for breeding, nutrition and health management decisions in pasture-based dairy systems

Adediran, S.A., Malau-Aduli, A.E.O., Roche, J.R., and Donaghy, D.J. (2007) Using lactation curves as a tool for breeding, nutrition and health management decisions in pasture-based dairy systems. Current Topics in Dairy Production, 12. pp. 74-78.

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Abstract

Milk yield and reproductive efficiency are crucial to profitable dairying. Although, genetic improvement in the last few decades has led to substantial increases in milk yield/cow, fertility and reproductive health have declined. Although estimated breeding values (EBV) in dairy cows in Australia incorporate indices of economic value, such as survival and milking speed, the impact of the current breeding approach and management on the shape of the lactation profile is not clear. Mathematical functions such as those previously used to describe a series of milk test day records have the advantage of minimizing random variation while simultaneously summarising the lactation profile into biologically interpretable parameters. The shape of the lactation curve provides valuable information about the biological and economic efficiency of the animal or herd and is useful for genetic evaluation, health monitoring, feed management decisions and planning purposes. A cow’s genetic merit, breed,parity, calving season, nutrition, and pregnancy affect the shape of her lactation curve. A robust model should adequately mimic the biological process of lactation and adjust for factors affecting it. The objective of this study was to identify suitable lactation models and determine the factors affecting the lactation profile, in pasture-based dairy systems. Test-day milk yield data from 428 herds in Tasmania, consisting of 65,000 milk yield records from 2002-2005, were edited to exclude incomplete lactations. Five empirical and two mechanistic functions were used to evaluate average daily milk. Mechanistic models performed best with herd data and offered insights into the physiological basis of lactation, while the polynomial regression models fitted better overall. All the models, except modified gamma, equally well portrayed the lactation profile. Parameter estimates were significant (P<0.05), with large serial correlations indicating biased predictions at various lactation stages.Lactation curves of individual cow milk yields were more varied and exhibited the tendency for a second peak which was not accurately modeled.

Item ID: 55015
Item Type: Article (Short Note)
ISSN: 1326-849X
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Proceedings of the Dairy Research Foundation Symposium, Sydney, NSW, Australia, 2007.

Date Deposited: 04 Dec 2018 02:06
FoR Codes: 07 AGRICULTURAL AND VETERINARY SCIENCES > 0702 Animal Production > 070201 Animal Breeding @ 100%
SEO Codes: 83 ANIMAL PRODUCTION AND ANIMAL PRIMARY PRODUCTS > 8303 Livestock Raising > 830302 Dairy Cattle @ 100%
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