Investigating causal biological relationships between reproductive performance traits in high-performing gilts and sows1

Abstract

Efficient management of swine production systems requires understanding of complex reproductive physiological mechanisms. Our objective in this study was to investigate potential causal biological relationships between reproductive performance traits in high-producing gilts and sows. Data originated from a nutrition experiment and consisted of 200 sows and 440 gilts arranged in body weight blocks and randomly assigned to dietary treatments during late gestation at a commercial swine farm. Reproductive performance traits consisted of weight gain during late gestation, total number born and number born alive in a litter, born alive average birth weight, wean-to-estrous interval, and total litter size born in the subsequent farrowing. Structural equation models combined with the inductive causation algorithm, both adapted to a hierarchical Bayesian framework, were employed to search for, estimate, and infer upon causal links between the traits within each parity group. Results indicated potentially distinct reproductive networks for gilts and for sows. Sows showed sparse connectivity between reproductive traits, whereas the network learned for gilts was densely interconnected, suggesting closely linked physiological mechanisms in younger females, with a potential for ripple effects throughout their productive lifecycle in response to early implementation of tailored managerial interventions. Cross-validation analyses indicated substantial network stability both for the general structure and for individual links, though results about directionality of such links were unstable in this study and will need further investigation. An assessment of relative statistical power in sows and gilts indicated that the observed network discrepancies may be partially explained on a biological basis. In summary, our results suggest distinctly heterogeneous mechanistic networks of reproductive physiology for gilts and sows, consistent with physiological differences between the groups. These findings have potential practical implications for integrated understanding and differential management of gilts and sows to enhance efficiency of swine production systems.

Keywords: hierarchical Bayesian models; structural equation model; structure learning; swine reproductive physiology.