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 7256 pigs, from two Piétrain paternal lines (Pie and Pie NN), were tested at the AXIOM boar testing station (Azay-sur-Indre, France) in 2019-2021. During the fattening period (from 75 to 150 days of age), individual performance indicators were recorded (growth, backfat, loin depth, feed intake, feed conversion ratio) 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 ±0.03 for Pie NN line and a value of 0.09 ±0.02 for Pie line, compared to 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 ±0.02 to 0.07±0.03). Genetic correlations between R2 and R3 were high and favourable (0.93 ±0.04 to 0.95 ±0.03) and R2 and R3 can be considered as identical with regard to the confidence interval. These two robustness scores were also highly and favourably genetically correlated with initial body weight and average daily gain, and unfavourably correlated with daily feed intake (ranging from 0.73 ±0.06 to 0.90 ±0.08). Estimates of genetic correlations of R2 and R3 with backfat depth and raw feed conversion ratio (not standardized between starting and finishing weights) were moderate and unfavorable (0.20 ±0.13 to 0.46±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.

Quantifying growth perturbations over the fattening period in swine via mathematical modelling, PeerCommunity Journal, 2: e9.

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Background: Resilience can be defined as the capacity of animals to cope with short-term perturbations in their environment and return rapidly to their pre-challenge status. In a perspective of precision livestock farming, it is key to create informative indicators for general resilience and therefore incorporate this concept in breeding goals. In the modern swine breeding industry, new technologies such as automatic feeding system are increasingly common and can be used to capture useful data to monitor animal phenotypes such as feed efficiency. This automatic and longitudinal data collection integrated with mathematical modelling has a great potential to determine accurate resilience indicators, for example by measuring the deviation from expected production levels over a period of time. Results: This work aimed at developing a modelling approach for facilitating the quantification of pig resilience during the fattening period. A total of 13 093 pigs, belonging to three different genetic lines were monitored, and body weight measures registered with automatic feeding systems. We used the Gompertz model and linear interpolation on body weight data to quantify individual deviations from expected production, thereby creating a resilience index. The approach was able to quantify different degrees of perturbation. Further, we evaluated the heritability of the resilience index in the different lines analyzed. Conclusions: Our model-based approach can be useful to quantify pig responses to perturbations using exclusively the growth curves and should contribute to the improvement of swine productive performance. Keywords: modelling, perturbation, resilience, robustness, body weight, big data, pig, precision livestock farming

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 Pietrain 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. 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. 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.

The objectives of this study were to investigate the possibility of characterising animal robustness by using indicators based on the dynamics of energy allocation of the animal and to determine their genetic parameters. A total of 2 140 pigs, from the Piétrain NN Français line, were raised at the AXIOM boar testing station. 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 factor (αt) between cumulative net energy available, estimated from feed intake, and cumulative weight gain during fattening period. The variance of αt, that could be interpreted as an indicator of the response of the animal to perturbations/stress, showed moderate heritability (0.27 ±0.08). Our perspective is to further decompose the allocation factor into components to better characterise the robustness phenotype.