Quantifying growth perturbations over the fattening period in swine via mathematical modelling

Revilla, Manuel; Lenoir, Guillaume; Flatres-Grall, Loïc; Muñoz-Tamayo, Rafael; Friggens, Nicolas

doi:10.24072/pcjournal.82

Cited by 12 publications

(14 citation statements)

References 22 publications

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“…Here, resilience indicator traits are based on deviations of production performance from the undisturbed phenotype (here defined as the contemporary group average). An alternative is to estimate the undisturbed phenotype of an individual based on various modeling approaches [ 14 , 16 , 50 ]. Both approaches may underestimate the undisturbed phenotype, leading to underestimation of deviations and, thus, overestimation of resilience.…”

Section: Discussionmentioning

confidence: 99%

Using egg production longitudinal recording to study the genetic background of resilience in purebred and crossbred laying hens

et al. 2022

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Background There is growing interest in using genetic selection to obtain more resilient farm animals (i.e. that are minimally affected by disturbances or rapidly recover from them). The aims of this study were to: (i) estimate the genetic parameters of resilience indicator traits based on egg production data, (ii) assess whether these traits are genetically correlated in purebreds and crossbreds, and (iii) assess the genetic correlations of these traits with egg production (EP) as total number of eggs between 25 and 83 weeks. Purebred hens (33,825 from a White Leghorn (WA) line and 34,397 from a Rhode Island (BD) line were housed in individual cages, while crossbred hens were housed in collective cages of 6 to 8 paternal half-sibs (12,852 WA and 3898 BD crossbred groups, where the name of the group refers to the line used as the sire). Deviations of a hen’s weekly egg production from the average of the corresponding batch were calculated. Resilience indicator traits investigated were the natural logarithm of the variance (LNVAR), the skewness (SKEW), and the lag-one autocorrelation (AUTO-R) of these deviations. Results In both purebred lines, EP was estimated to be lowly heritable (WA: 0.11 and BD: 0.12). Resilience indicators were also estimated to be lowly heritable in both lines (LNVAR: 0.10 and 0.12, SKEW: 0.04 and 0.02, AUTO-R: 0.06 and 0.08 in WA and BD, respectively). In both crossbred groups, EP, AUTO-R, and SKEW were estimated to be less heritable than in purebreds (EP: $$h^{2}$$ h 2 ≤ 0.07; and resilience indicator traits: $$h^{2}$$ h 2 ≤ 0.03), while LNVAR had an $$h^{2}$$ h 2 estimate that was similar to or higher in crossbreds ($$h^{2}$$ h 2 ranged from 0.13 to 0.21) than in purebreds. In both purebreds and crossbreds, resilience indicator traits were estimated to have favorable genetic correlations with EP and between each other. For all traits and in both lines, estimates of genetic correlations between purebreds and crossbreds ($$r_{pc}$$ r pc ) differed from 1 and ranged from 0.16 to 0.63. Conclusions These results show that selection for resilience based on EP data can be considered in breeding programs for layers. Genetic improvement of resilience in crossbreds can be achieved by using information on purebreds, but would be greatly enhanced by the integration of information on crossbreds in breeding programs.

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Section: Discussionmentioning

confidence: 99%

Using egg production longitudinal recording to study the genetic background of resilience in purebred and crossbred laying hens

et al. 2022

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“…The quantification of robustness or resilience through the analysis of variations in feed intake have also been studied (Putz et al, 2019; Nguyen-Ba et al, 2020; Homma et al, 2021). With respect to robustness, the effect of disturbances on growth pattern has been studied on pigs after weaning (Revilla et al, 2019) or during the finishing period (Revilla et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…A pre-treatment process was performed on BW and FI, recorded each time the animal went into the AFS, to validate them, identify quality issues and convert them on a daily scale. This process followed the procedure proposed by Revilla et al (2022) and modified by Lenoir et al (2022a). In summary, on the scale of the visit, a quadratic regression of BW on age + age 2 for each animal was applied to eliminate aberrant BW.…”

Section: Methodsmentioning

confidence: 99%

“…The acquisition of temporal data of feed intake and weight in growing pigs made it possible to consider the development of allocation model based on these two variables to caracterize robustness. With this objective, we developed a conceptual model to represent the temporal pattern of allocation of energy intake to growth in fattening pigs (Lenoir et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…This offers the possibility to characterize the dynamics of those phenotypes for each individual in the face of variations in the environment. Several studies have used such longitudinal data to quantify resilience indicators based on deviation between an expected trajectory of each individual for a given non-perturbed environment and its observed trajectory on feed intake (Nguyen-Ba et al, 2020) or body weight (Revilla et al, 2022). Definition and modelling of individual potential trajectory are challenging issues in these approaches.…”

Section: Introductionmentioning

confidence: 99%

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

Lenoir

Flatres-Grall²,

Muñoz-Tamayo

et al. 2022

Preprint

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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.

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Impact of environmental disturbances on estimated genetic parameters and breeding values for growth traits in pigs

Rohmer

David

2022

Animal

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Quantifying growth perturbations over the fattening period in swine via mathematical modelling

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

Cited by 12 publications

References 22 publications

Using egg production longitudinal recording to study the genetic background of resilience in purebred and crossbred laying hens

Using egg production longitudinal recording to study the genetic background of resilience in purebred and crossbred laying hens

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

Impact of environmental disturbances on estimated genetic parameters and breeding values for growth traits in pigs

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