Disentangling the dynamics of energy allocation to provide a proxy of robustness in fattening pigs

Background There is a growing need to improve robustness characteristics in fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a robustness proxy on the basis of modelling of longitudinal energetic allocation coefficient to growth for fattening pigs. Consequently, the environmental variance of this allocation coefficient was considered as a proxy of robustness. The second objective was to estimate its genetic parameters and correlation with traits under selection as well with phenotypes routinely collected on farms. A total of 5848 pigs, from Piétrain NN paternal line, were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm was equipped with automatic feeding system, recording individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between cumulative net energy available, estimated from feed intake, and cumulative weight gain during fattening period. Longitudinal energetic allocation coefficients were analysed using a two-step approach, to estimate both its genetic variance and the genetic variance in the residual variance, trait LSR. Results The LSR trait, that could be interpreted as an indicator of the response of the animal to perturbations/stress, showed low heritability (0.05±0.01). The trait LSR had high favourable genetic correlations with average daily growth (−0.71±0.06) and unfavourable with feed conversion ratio (−0.76±0.06) and residual feed intake (−0.83±0.06). The analysis of the relationship between estimated breeding values (EBV) LSR quartiles and phenotypes routinely collected on farms shows the most favourable situation for animals from quartile with the weakest EBV LSR, i.e ., the most robust. Conclusions These results show that selection for robustness based on deviation from energetic allocation coefficient to growth can be considered in breeding programs for fattening pigs. ### Competing Interest Statement RMT, NF and ID declare that they have no competing interests. GL and LFG are employed by AXIOM. The datasets are of interest to commercial targets of AXIOM, but this interest did not influence the results in this manuscript in any matter. * α t : daily energetic allocation coefficient to growth ADG : average daily growth AFS : automatic feeding system AMW : average metabolic weight BF : backfat thickness BF100 : backfat thickness estimated at 100 kg liveweight BW : body weight CNEA : cumulative net energy available for growth CW : cumulative weight gain DFI : daily feed intake DLM : dynamic linear model EBV : estimated breeding value EI : net energy intake FCR : feed conversion ratio FI : feed intake IBW : initial body weight LD : longissimus dorsi thickness LD100 : longissimus dorsi thickness estimated at 100 kg liveweight LSR : log transformed squared residuals, robustness indicator MR : net energy maintenance requirement NEA : net energy available for growth PDFI : potential average daily feed intake Pie NN : Piétrain NN Français free from halothane-sensitivity RFI : residual feed intake TBW : body weight at individual testing