Dynamic data of body weight and feed intake in fattening pigs, and the determination of energetic allocation factors using a dynamic linear model

Lenoir, G.; Kashefifard, K.; Chesnet, C.; Flatres-Grall, Loïc; Muñoz-Tamayo, Rafael

doi:10.1016/j.anopes.2022.100014

Cited by 3 publications

(3 citation statements)

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“…A dynamic linear regression model (DLM) [ 29 ] was used to estimate the daily energy allocation coefficient to growth ( ) [ 17 ]. First, was converted into net energy intake in MJ ( ), using the net energy density of the feed, i.e.…”

Section: Methodsmentioning

confidence: 99%

“…Acquisition of temporal data on feed intake and weight in growing pigs has made it possible to consider the development of an allocation model based on these two variables to characterize robustness. With this objective, we recently developed a conceptual model to represent the temporal pattern of the allocation of energy intake to growth in fattening pigs [ 17 ]. Using this model, our first objective in the present study was to develop and evaluate a robustness indicator based on the modelling of longitudinal energy allocation coefficients to growth in fattening pigs.…”

Section: Introductionmentioning

confidence: 99%

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

Lenoir,

Flatres-Grall,

Muñoz-Tamayo

et al. 2023

Genet Sel Evol

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Lenoir,

Flatres-Grall,

Muñoz-Tamayo

et al. 2023

Genet Sel Evol

View full text Add to dashboard Cite

show abstract

“…A dynamic regression model (DLM; West and Harrison, 1997) was used to estimate daily energetic allocation coefficient to growth (α it ; Lenoir et al, 2022b). First, FI it was converted in net energy intake in MJ (EI it ), using the net energy density of the feed of 9.85 MJ of NE/kg.…”

Section: Modelling Energetic Allocation Coefficient To Growthmentioning

confidence: 99%

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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Getting to grips with resilience: Toward large-scale phenotyping of this complex trait

Friggens,

Ithurbide,

Lenoir

2024

JDS Communications

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Graphical Abstract Summary: The resilience of an animal can be compared with the shock absorbers of a car, which buffer road bumpiness. Just as the quality of a shock absorber is measured by its ability to respond to road conditions, measuring animal resilience requires capturing the response to environmental challenges through time-series measurements. On farm precision livestock technologies offer valuable opportunities in this regard. Environmental disturbances can be diverse, and the relative importance of different underlying resilience mechanisms (depicted by K and C) can change; snow tires are useful in winter but not on sand. Understanding the mechanisms may enable us to construct more nuanced resilience phenotypes. To validate a new shock absorber, we need long-term customer feedback. Likewise, resilience indicators need to be validated against the cumulative consequences of resilience, such as functional longevity.

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Dynamic data of body weight and feed intake in fattening pigs, and the determination of energetic allocation factors using a dynamic linear model

Cited by 3 publications

References 0 publications

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

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

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

Getting to grips with resilience: Toward large-scale phenotyping of this complex trait

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