In: Bill Fulkerson (Editor) Current Topics in Dairy Production, Volume 12’, Proceedings of the Dairy Research Foundation Symposium, The University of Sydney, Australia, 74-78 (2007) 74 Using lactation curves as a tool for breeding, nutrition and health management decisions in pasture-based dairy systems. S.A. Adediran 1 ,A.E.O. Malau-Aduli 1 , J. R. Roche 1,2 , and D.J. Donaghy 1,2 1 School of Agricultural Science, University of Tasmania, Private Bag 54 Hobart, TAS 7001 Australia. 2 Tasmanian Institute of Agricultural Research, P.O. Box 3523, Burnie, TAS 7320. samuel.adediran@utas.edu.au Introduction 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 (Dematawewa and Berger, 1998). In a pasture-based system, a 365 day calving interval is crucial for optimum profit. Hence the need to increase milk yield by improving persistence of lactation rather than peak lactation which puts increased stress on the cows at the time when they should be rebreeding. Peak milk yield, persistency and lactation length are the key components of the lactation profile. Dairy cows with high peak yields are more prone to metabolic and physiological disorders (Terkeli et al 1999). Although estimated breeding values (EBV) in dairy cows in Australia incorporates 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 (Wood, 1967, Wilmink, 1987), 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 (Sherchand et al., 1995). Cow’s genetic merit, breed, parity, calving season, nutrition, and pregnancy affect the shape of her lactation curve (Tozer and Huffaker, 1999, Roche et al., 2006). A robust model should adequately mimic the biological process of lactation and adjust for factors affecting it.