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- W2138776732 abstract "Abstract Parameters of daily milk yield during the first three lactations of Chios ewes were estimated with random regression models. Data consisted of 42 675 test-day records of 7121 ewes from 75 flocks that had lambed between 1998 and 2000. Models fitted fourth order fixed regressions on Legendre polynomials of the number of days post partum and fourth order random regressions on the individual animal. (Co)variance components were estimated with Gibbs sampling. Lactations were analysed separately. The four eigen values accounted for 0·80 to 0·84, 0·11 to 0·15, 0·04 to 0·05 and about 0·01 of the animal variance, respectively, depending on lactation number. Animal variance estimates, including genetic and, partly, permanent environment effects, were high at the beginning of each lactation and decreased as lactation progressed, suggesting that the animal effect is most important to early daily records. Residual variance was highest in the middle of lactation, suggesting that non-systematic environmental factors play a bigger at that time. Animal correlation estimates between daily yield records ranged from 0·26 to 0·99, were highest for adjacent days and decreased for days further apart. The decline had a different shape in the three lactations and was more evident in the first, suggesting that the three lactations may be biologically distinct traits. Animal correlation estimates between daily and total lactation milk yield ranged from 0·61 to 0·98 and were highest in the middle and lowest towards the end of lactation. Early lactation daily yield had an animal correlation of 0·70 to 0·80 with total lactation milk yield, in all three lactations. Results of this study suggest that daily milk yield records in the early stages of lactation may be useful for selection of ewes with high producing ability and accurate prediction of total lactation milk yield." @default.
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- W2138776732 date "2005-10-01" @default.
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- W2138776732 title "Population parameter estimation of daily milk yield of the Chios sheep using test-day random regression models and Gibbs sampling" @default.
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- W2138776732 doi "https://doi.org/10.1079/asc50030233" @default.
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