Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs.

BACKGROUND: There is a growing need to improve robustness of fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a proxy for robustness of fattening pigs by modelling the longitudinal energy allocation coefficient to growth, with the resulting environmental variance of this allocation coefficient considered as a proxy for robustness. The second objective was to estimate its genetic parameters and correlations with traits under selection and with phenotypes that are routinely collected. In total, 5848 pigs from a Pietrain NN paternal line were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm is equipped with an automatic feeding system that records individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between the available cumulative net energy, which was estimated from feed intake, and cumulative weight gain during the fattening period. Longitudinal energy allocation coefficients were analysed using a two-step approach to estimate both the genetic variance of the coefficients and the genetic variance in their residual variance, which will be referred to as the log-transformed squared residual (LSR). RESULTS: The LSR trait, which could be interpreted as an indicator of the response of the animal to perturbations/stress, showed a low heritability (0.05 ± 0.01), a high favourable genetic correlation with average daily growth (- 0.71 ± 0.06), and unfavourable genetic correlations with feed conversion ratio (- 0.76 ± 0.06) and residual feed intake (- 0.83 ± 0.06). Segmentation of the population in four classes using estimated breeding values for LSR showed that animals with the lowest estimated breeding values were those with the worst values for phenotypic proxies of robustness, which were assessed using records routinely collected on farm. CONCLUSIONS: Results of this study show that selection for robustness, based on estimated breeding values for environmental variance of the allocation coefficients to growth, can be considered in breeding programs for fattening pigs.

Robustness scores in fattening pigs based on routinely collected phenotypes: determination and genetic parameters.

The objective was to determine operational proxies for robustness based on data collected routinely on farm that allow phenotyping of these traits in fattening pigs, and to estimate their genetic parameters. A total of 7,256 pigs, from two Piétrain paternal lines (Pie and Pie NN), were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2019 to 2021. During the fattening period (from 75 to 150 d of age), individual performance indicators were recorded (growth, backfat, loin depth, feed intake, and feed conversion ratio [FCR]) together with indicators such as insufficient growth, observable defect, symptoms of diseases, and antibiotic and anti-inflammatory injections. These indicators were combined into three categorical robustness scores: R1, R2, and R3. Genetic parameters were estimated using an animal linear model. The robustness score R2 (selectable or not selectable animal) that combined information from status at testing and mortality had the highest heritability estimates of 0.08 ±â€…0.03 for Pie NN line and a value of 0.09 ±â€…0.02 for Pie line, compared with traits R1 and R3. The score R3 that combines information from the score R2 with antibiotic and anti-inflammatory injections presented slightly lower heritability estimates (0.05 ±â€…0.02 to 0.07 ±â€…0.03). Genetic correlations between R2 and R3 were high and favorable (0.93 ±â€…0.04 to 0.95 ±â€…0.03) and R2 and R3 can be considered identical with regard to the confidence interval. These two robustness scores were also highly and favorably genetically correlated with initial body weight and average daily gain, and unfavorably correlated with daily feed intake (ranging from 0.73 ±â€…0.06 to 0.90 ±â€…0.08). Estimates of genetic correlations of R2 and R3 with backfat depth and raw FCR (not standardized between starting and finishing weights) were moderate and unfavorable (0.20 ±â€…0.13 to 0.46 ±â€…0.20). A part of these genetic correlations, that are of low precision due to the number of data available, have to be confirmed on larger datasets. The results showed the interest of using routine phenotypes collected on farm to build simple robustness indicators that can be applied in breeding.

The objective was to determine operational proxies for robustness based on data collected routinely on farm that allow phenotyping of these traits in fattening pigs (from approximately 75 to 150 d of age), and to estimate their genetic parameters. A total of 7,256 pigs, from two Piétrain paternal lines (Pie and Pie NN), were tested. Individual performance indicators were recorded together with indicators such as insufficient growth, observable defects, symptoms of diseases, and antibiotic and anti-inflammatory injections. These indicators were combined into three categorical robustness scores: R1, R2, and R3. The robustness score R2 (selectable or not selectable animal) that combined information from status at testing and mortality had the highest heritability of 0.08 ±â€…0.03 for Pie NN line and a value of 0.09 ±â€…0.02 for Pie line. This robustness score was also highly and favorably genetically correlated with initial body weight and average daily gain, and unfavorably correlated with daily feed intake in both lines (ranging from 0.73 ±â€…0.06 to 0.90 ±â€…0.08). Estimates of genetic correlations of R2 with backfat depth and feed conversion ratio were moderate and unfavorable (0.20 ±â€…0.13 to 0.46 ±â€…0.20). The results showed the interest of using routine phenotypes collected on farm to build simple robustness indicators that can be applied in breeding.

Prospects for the Analysis and Reduction of Damaging Behaviour in Group-Housed Livestock, With Application to Pig Breeding.

Innovations in the breeding and management of pigs are needed to improve the performance and welfare of animals raised in social groups, and in particular to minimise biting and damage to group mates. Depending on the context, social interactions between pigs can be frequent or infrequent, aggressive, or non-aggressive. Injuries or emotional distress may follow. The behaviours leading to damage to conspecifics include progeny savaging, tail, ear or vulva biting, and excessive aggression. In combination with changes in husbandry practices designed to improve living conditions, refined methods of genetic selection may be a solution reducing these behaviours. Knowledge gaps relating to lack of data and limits in statistical analyses have been identified. The originality of this paper lies in its proposal of several statistical methods for common use in analysing and predicting unwanted behaviours, and for genetic use in the breeding context. We focus on models of interaction reflecting the identity and behaviour of group mates which can be applied directly to damaging traits, social network analysis to define new and more integrative traits, and capture-recapture analysis to replace missing data by estimating the probability of behaviours. We provide the rationale for each method and suggest they should be combined for a more accurate estimation of the variation underlying damaging behaviours.

The direct-maternal genetic correlation has little impact on genetic evaluations.

Obtaining unbiased estimates of the direct-maternal genetic correlation proves far from straightforward for several reasons. Consequently, the use of such over- or underestimated correlations may introduce errors in genetic evaluation models. The objective of our study was to evaluate how the value of the direct-maternal genetic correlation affects EBV. Direct, maternal, and total breeding values were predicted for the ADG or weight at weaning for 3 different species (sheep, rabbits, and pigs) using models that differ depending on the fixed value of the direct-maternal genetic correlation (ranging from -0.9 to 0.9) as well as a model in which the correlation was estimated. The results were consistent between species. The direct-maternal genetic correlation had a greater impact on the estimated maternal genetic effects than on direct effects. The lowest correlations between maternal breeding values obtained with different models were -0.20, -0.01, and -0.72 in pigs, sheep, and rabbits, respectively, whereas for the direct breeding value, the lowest correlations were 0.45, 0.90, and 0.95 in pigs, sheep, and rabbits, respectively. The total EBV, calculated as the unweighted sum of direct and maternal genetic effects, did not differ greatly between the models, the lowest correlations between total breeding values being 0.93, 0.98, and 0.97 for pigs, sheep, and rabbits, respectively. Given the uncertainty associated with estimating the direct-maternal genetic correlation, setting its value to 0 in genetic evaluation models appears to be a good compromise.