Robustness scores in fattening pigs estimated from phenotypes measured routinely: definition and genetic parameters
- 1. Université Paris-Saclay, INRAE-AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 75005 Paris, France
- 2. AXIOM, La Garenne, 37310 Azay-sur-Indre, France
- 3. GenPhySE, INRAE, ENVT, Université de Toulouse, 31326, Castanet-Tolosan, France
Description
Abstract
The objective of this study was to define robustness scores based on data collected routinely on farm to allow phenotyping of this trait in fattening pigs. A total of 7 256 pigs, from 2 Piétrain paternal lines (Pie and Pie NN), were controlled 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 mortality, clinical signs, antibiotic and anti-inflammatory injections. These indicators were combined to estimate a set of three categorical robustness traits: R1, R2 and R3. Genetic parameters were estimated using an animal linear model. The robustness score R2 (selectable or not selectable animal) that combines information from status at testing and mortality presented the most interesting heritability, from 0.12 (±0.03) to 0.13 (±0.02) depending of the line. The score R3 that combines information from the score R2 with antibiotic and anti-inflammatory injections presented slightly lower heritability estimates (0.08 ±0.02 to 0.11±0.03). Genetic correlations between R2 and R3 were high and favorable (0.93 ±0.04 to 0.95 ±0.03). These two robustness scores were also highly and favorably genetically correlated with initial body weight, average daily gain and 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 feed conversion ratio 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.
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