4.7 Article

Genetic and phenotypic relationships of farrowing and weaning survival to birth and placental weights in pigs

期刊

JOURNAL OF ANIMAL SCIENCE
卷 84, 期 1, 页码 32-40

出版社

OXFORD UNIV PRESS INC
DOI: 10.2527/2006.84132x

关键词

birth weight; genetic parameter; pig; survival

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Data obtained during 4 generations of divergent selection for placental efficiency were used to determine factors influencing survival at farrowing and weaning in litters produced by first-parity females. Data were collected from 193 litters and included records on 2,053 individuals. Farrowing survival (FS) and weaning survival (WS) were considered traits of the piglet and were scored I if the individual was alive at a time point or 0 if dead. Estimates of (co)variance components for direct and maternal additive genetic effects for FS and WS were obtained using an animal model and computed with the MTDFREML program. Estimates of direct heritability were 0.16 for FS and 0.18 for WS. Estimates of maternal heritability were 0.14 for FS and 0.10 for WS. Genetic correlation estimates between direct and maternal effects were high and negative for both traits. The direct genetic correlation between FS and WS was 0.92. Variables associated with FS and WS were determined using logistic regression procedures. Birth weight (BRW), placental weight, their interaction, and total born can be used as predictors of survival at farrowing in the absence of estimates of genetic merit for survival. The same model, excluding total number born, was the best model for predicting WS. In the presence of BRW information, placental efficiency did not improve the prediction of survival. While it was clearly disadvantageous for a piglet to be below the litter mean in BRW, being above the mean did not provide a substantial advantage in survival. Results from this analysis suggest that it is possible to select for increased survival at farrowing and at weaning. Information on a piglet's BRW, placental weight, litter average BRW, and deviation from litter average BRW can be used to optimize those values at levels resulting in high survival probability.

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