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Piglet Morphology: Indicators of Neonatal Viability?

Bryony S TuckerKiro Risto PetrovskiJessica R CraigRebecca S MorrisonRobert J SmitsRoy Neville Kirkwood
Published in: Animals : an open access journal from MDPI (2022)
The morphological measures, crown-to-rump length (CR), and abdominal circumference (AC) have been suggested to be as good, if not better, than birth weight for predicting piglet performance. We explored the relationships between CR and AC, and piglet weights at birth and 24 h, to investigate their predictive value for piglet survival. Piglet weight and AC at birth and 24 h, and CR at 24 h were recorded for 373 piglets born to 31 sows. Morphological measures were categorised into two levels for weight and three levels for AC and CR. Further, AC and CR groupings were concatenated to create a new variable (PigProp) to describe the proportionality of piglet morphology. Proportionate piglets had equal CR and AC levels, and disproportionate piglets had contrasting levels. Birth AC was a good predictor of colostrum intake ( p < 0.001) when accounting for birth weight, but 24 h weight and PigProp were good indicators of actual colostrum intake ( p < 0.001 for both). The significant interaction of colostrum and PigProp showed that within the smaller piglet groups, those who had greater than 200 g of colostrum had higher 24 h weight and survival ( p < 0.001 both). As expected, as body weight and colostrum intake increased, so did weight change to d 21 (P = 0.03 and trend at p = 0.1, respectively). A similar pattern was seen with increasing PigProp group ( p < 0.001); however, piglets from the disproportionate group 1,3 had the greatest observed weight change (5.15 ± 0.06 kg). Our data show morphological measures may be more predictive of piglet viability in terms of both performance and survival than weight and there may be subgroups that have higher than expected chances of survival.
Keyphrases
  • weight gain
  • birth weight
  • body weight
  • body mass index
  • gestational age
  • weight loss
  • physical activity
  • human milk
  • preterm birth
  • pregnant women
  • electronic health record
  • machine learning