An association analysis of sow parity, live-weight and back-fat depth as indicators of sow productivity.
Anna LaveryP G LawlorE MagowanH M MillerK O'DriscollD P BerryPublished in: Animal : an international journal of animal bioscience (2018)
Understanding how critical sow live-weight and back-fat depth during gestation are in ensuring optimum sow productivity is important. The objective of this study was to quantify the association between sow parity, live-weight and back-fat depth during gestation with subsequent sow reproductive performance. Records of 1058 sows and 13 827 piglets from 10 trials on two research farms between the years 2005 and 2015 were analysed. Sows ranged from parity 1 to 6 with the number of sows per parity distributed as follows: 232, 277, 180, 131, 132 and 106, respectively. Variables that were analysed included total born (TB), born alive (BA), piglet birth weight (BtWT), pre-weaning mortality (PWM), piglet wean weight (WnWT), number of piglets weaned (Wn), wean to service interval (WSI), piglets born alive in subsequent farrowing and sow lactation feed intake. Calculated variables included the within-litter CV in birth weight (LtV), pre-weaning growth rate per litter (PWG), total litter gain (TLG), lactation efficiency and litter size reared after cross-fostering. Data were analysed using linear mixed models accounting for covariance among records. Third and fourth parity sows had more (P0.05). Heavier sow live-weight throughout gestation was associated with an increase in PWM (P0.05). In conclusion, this study showed that sow parity, live-weight and back-fat depth can be used as indicators of reproductive performance. In addition, this study also provides validation for future development of a benchmarking tool to monitor and improve the productivity of modern sow herd.
Keyphrases
- gestational age
- birth weight
- weight gain
- body mass index
- weight loss
- physical activity
- adipose tissue
- preterm infants
- climate change
- body weight
- low birth weight
- mental health
- type diabetes
- human milk
- fatty acid
- intensive care unit
- mycobacterium tuberculosis
- coronary artery disease
- cardiovascular events
- machine learning
- big data
- dairy cows