Deliverable D5.5 Demonstration of the use of social effects and crossbred and genomic information to improve selection and feed efficiency

Objectives

The rate of improvement in feed efficiency in Europe is, time and again, lower than it could be. Increases in slaughter weight, non-castration, feed prices, and other factors obscure the trend. This does not mean that all these factors have a negative impact on feed efficiency. Non-castration for example improves feed efficiency. In this deliverable we studied if (1) understanding group dynamics can help to increase rate of improvement (it hardly does) and (2) if adding field data (i.e., crossbred data, via the use of genomics) to data collected on selection candidates can help to increase accuracy of selection (it does). Information collected on purebred animals has partially, for largely unknown reasons, a different genetic background, which can be captured by proper modelling and the use of genomics.

Rationale

Most livestock animals are kept in groups. The group dynamics can lead to damaging behavior and can, more subtle, also positively influence performance parameters. There is very good evidence that genetic variation in indirect genetic effects exists, and that it can be validated in masked data. Attempts to show added value in a commercial environment are mostly unsuccessful. A novel approach, relating feed intake data to social ranking shows promise, especially since this feed intake data is available on a large scale. Another novel approach, the analysis of culled rearing gilts because of damage on ears or tails, shows considerable genetic variation.
The application of the crossbred genomic toolset on protein deposition in crossbred finishers yielded a high/low contrast in which clear differences in nitrogen efficiency were shown. This is a proof of principle valuable for a different definition of feed efficiency, not in terms of energy, but in terms of protein or nitrogen.

In conclusion, the genetic and genomic toolset is continuously improving and focusing more and more on the final product. Social interactions are important for behavior, can be statistically described, and have a genetic background. Improvement in feed efficiency will come, for now, more from the physiological than from the behavioral side. Genomic prediction of production traits is improving each year and predicts future performance of young animals better and better.