Adapting the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems
Adapting the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems

Genetic parameters and expected responses to selection for components of feed efficiency in a Duroc pig line

Authors: 
Sánchez J.P., Ragab M., Quintanilla R., Rothschild M.F.; Piles M.
Publication date: 
1 December 2017
Full title: 
Genetic parameters and expected responses to selection for components of feed efficiency in a Duroc pig line
Publishing information: 
Genetics Selection Evolution, 2017, 49:86
Abstract: 

Background

Improving feed efficiency (FEFE) is a key factor for any pig breeding company. Although this can be achieved by selection on an index of multi-trait best linear unbiased prediction of breeding values with optimal economic weights, considering deviations of feed intake from actual needs (RFIRFI) should be of value for further research on biological aspects of FEFE. Here, we present a random regression model that extends the classical definition of RFIRFI by including animal-specific needs in the model. Using this model, we explore the genetic determinism of several FEFE components: use of feed for growth (WGWG), use of feed for backfat deposition (FGFG), use of feed for maintenance (MWMW), and unspecific efficiency in the use of feed (RFIRFI). Expected response to alternative selection indexes involving different components is also studied.

Results

Based on goodness-of-fit to the available feed intake (FIFI) data, the model that assumes individual (genetic and permanent) variation in the use of feed for maintenance, WGWG and FGFG showed the best performance. Joint individual variation in feed allocation to maintenance, growth and backfat deposition comprised 37% of the individual variation of FIFI. The estimated heritabilities of RFIRFI using the model that accounts for animal-specific needs and the traditional RFIRFI model were 0.12 and 0.18, respectively. The estimated heritabilities for the regression coefficients were 0.44, 0.39 and 0.55 for MWMW, WGWG and FGFG, respectively. Estimates of genetic correlations of RFIRFI were positive with amount of feed used for WGWG and FGFG but negative for MWMW. Expected response in overall efficiency, reducing FIFI without altering performance, was 2.5% higher when the model assumed animal-specific needs than when the traditional definition of RFIRFI was considered.

Conclusions

Expected response in overall efficiency, by reducing FIFI without altering performance, is slightly better with a model that assumes animal-specific needs instead of batch-specific needs to correct FIFI. The relatively small difference between the traditional RFIRFI model and our model is due to random intercepts (unspecific use of feed) accounting for the majority of variability in FIFI. Overall, a model that accounts for animal-specific needs for MWMW, WGWG and FGFG is statistically superior and allows for the possibility to act differentially on FEFE components.

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