Neil Clelland

Project: Use of computer tomography based predictors of meat quality in sheep breeding programmes

Type: PHD

Institution: Scottish Agricultural College

Meat eating quality traits (MEQ, e.g. tenderness, juiciness) are known to be linked to fat levels, in different livestock species, largely due to positive associations with intra-muscular fat (IMF).

Since IMF and carcass fat are genetically highly positively correlated, intense selection for increased lean growth and reduced fatness may compromise MEQ. However, there is evidence that selection against carcass fat whilst maintaining IMF should be possible, as both fat depots are partially under independent genetic control.

The sheep industry has made substantial progress in reducing carcass fat relative to carcass weight and lean meat yield in terminal sire breeds. However, it must be ensured that carcass and meat composition is not changed by selection in a way that is detrimental to MEQ, which would reduce customer satisfaction.

X-ray computed tomography (CT) can measure fat, muscle and bone in live animals and CT predictions of carcass composition have been used in commercial UK sheep breeding programmes over the last decade. The use of spiral CT scanning technology, which can capture detailed three-dimensional information, may allow further advances in predicting aspects of meat quality, which have not been investigated to date.

CT provides the means to quantify both IMF (and potentially other MQ traits) and carcass fat in live animals at the same time, which could be exploited in selection programmes. The best way to use this technology in breeding programmes including meat quality and meat eating quality traits has not been fully investigated. Genetic parameters are required to enable these studies, including estimates of heritability for CT predictor traits for MQ and correlations with other relevant traits.

The optimal design of such breeding programmes would also need to be investigated before the use of CT predictions of meat quality traits can be  fully assessed.