Improvement of feed efficiency can be achieved by genetic selection directly on feed to BW gain ratio or for alternative traits. In the present study, 2 different traits were explored in the growing rabbit and their heritability and genetic correlations with traits recorded between weaning (30 d) and 63 d of age: i) residual feed intake (RFI), to select animals having low ad libitum feed intake independently from their production level, and ii) ADG under restricted feeding (ADGR; with a restriction level of 80% compared with ad libitum feeding of a control group), to select animals having high growth rate despite limited feed intake. To study these traits, 2 rabbit lines were established named i) ConsoResidual line and ii) ADGrestrict line. Under ad libitum or restricted feeding, it comes to select animals that waste less energy for maintenance, metabolism, or activity and retain more for tissue deposition. The selection process was similar in both lines. Data comprised records from generations 0 to 6 for about 1,800 rabbits per line measured for their BW at weaning and 63 d of age (BW63) and their individual feed consumption. Under ad libitum feeding, the heritability estimates were moderate for RFI (0.16 ± 0.05), ADG (0.19 ± 0.05), and feed conversion ratio (FCR; 0.22 ± 0.05). The high genetic correlation estimated between RFI and FCR (0.96 ± 0.03) was in accordance with the literature. The genetic correlation between RFI and ADG traits was not significant. Thus, selection for low RFI with ad libitum feeding was confirmed as a potential trait to improve FCR and reduce feed intake, with little effect on ADG. To our knowledge, there is no previous selection experiment on growing rabbits with restricted feeding. Our heritability estimates for ADGR and feed conversion ratio under restricted feeding (FCRR) were moderate (0.22 ± 0.06 and 0.23 ± 0.07, respectively) and had very high negative genetic correlation. Both selection criteria were found with high and favorable genetic correlations with feed efficiency recorded under each feeding regimen. However, their different genetic correlations with BW at weaning and at 63 d of age (BW63R; respectively, 0.85 and 0.17 for RFI and -0.25 and 0.81 for ADGR) suggested different impacts on major production traits that need further analyses to decipher the relative advantages of the 2 selection criteria, together with interactions between genotypes and feeding regimen.

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With the development of automatic self-feeders and electronic identification, automated, repeated measurements of individual feed intake (FI) and BW are becoming available in more species. Consequently, genetic models for longitudinal data need to be applied to study FI or related traits. To handle this type of data, several flexible mixed-model approaches exist such as character process (CPr), structured antedependence (SAD), or random regression (RR) models. The objective of this study was to compare how these different approaches estimate both the covariance structure between successive measurements of FI and genetic parameters and their ability to predict future performances in 3 species (rabbits, ducks, and pigs). Results were consistent between species. It was found that the SAD and CPr models fit the data better than the RR models. Estimations of genetic and phenotypic correlation matrices were quite consistent between SAD and CPr models, whereas correlations estimated with the RR model were not. Structured antedependence and CPr models provided, as expected and in accordance with previous studies, a decrease of the correlations with the time interval between measurements. The changes in heritability with time showed the same trend for the SAD and RR models but not for the CPr model. Our results show that, in comparison with the CPr model, the SAD and RR models have the advantage of providing stable predictions of future phenotypes 1 wk forward whatever the number of observations used to estimate the parameters. Therefore, to study repeated measurements of FI, the SAD approach seems to be very appropriate in terms of genetic selection and real-time managements of animals.