Browsing by Author "Reis Lacerda Medeiros, Ana Carolina"

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    Genetic correlation between milk urea nitrogen and reproductive performance in seasonal grazing dairy cows : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science (Animal Science) at Massey University, Palmerston North, New Zealand
    (Massey University, 2020) Reis Lacerda Medeiros, Ana Carolina
    Milk urea nitrogen (MUN) concentration is closely related to blood urea nitrogen concentration and is an indicator of the level of crude protein ingested by the cow. Studies with cows in indoor systems have reported an antagonistic phenotypic relationship between MUN and fertility traits, but no studies have reported estimates of genetic correlation between MUN and fertility traits in pasture-based systems. The objective of this study was to estimate genetic parameters for MUN concentration, fertility and production traits in New Zealand dairy grazing cows from two herds. Milk test records were collected from 637 cows from once-a-day and twice-a-day milking dairy herds from Massey University, Palmerston North, New Zealand, during the production seasons of 2016-17 and 2017-18. The average concentration of MUN ranged between 8.2 and 11.4 mg/dL with estimates of heritability and repeatability of 0.24 (0.09 SE) and 0.45 (0.05 SE). There were no significant heterosis nor breed effect for MUN. The estimates of genetic correlations between MUN and fertility traits (submission and pregnancy rate during the first 21 days after the start of mating) were moderate negative (-0.55 and -0.45), but the standard errors of the estimates were large due to the small data set. The phenotypic correlations were close to zero. The estimates of genetic parameters indicate that MUN concentration in milk can be reduced by genetic selection with a potential to increase the submission and pregnancy rate during the first 21 days after the start of mating, which are the two most important reproductive traits in grazing dairy cows. Further studies with a larger dataset would enable more accurate estimates of the genetic parameters.