Research Partner: Scotland’s Rural College (SRUC)

Project Duration: November 2011 – October 2014

Category: Sheep

PhD Student: Neil Clelland

Neil Clelland
The Problem:

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.

 

Aims and Objectives:

This project aimed to test the hypotheses that:

  • CT scanning can provide an accurate method for predicting meat quality traits in terminal sire sheep; and
  • these predictors can be incorporated into breeding programmes, to allow continued improvements in growth and carcase composition, whilst maintaining or improving lamb meat quality.

 

The objectives of the project were to:

  1. review current literature on CT scanning, in vivo predictions of meat quality and possibilities for their inclusion in breeding programmes
  2. investigate the best image analysis methods (using 2D and 3D CT information), and resulting parameters, for predicting different meat quality traits (e.g. IMF, shear force, taste panel traits)
  3.  estimate genetic parameters for the best CT predictors of MQ traits in the Texel breed (and potentially others), using historical commercial and research CT data
  4. investigate strategies to include CT predictors of MQ in breeding programmes for Texel sheep.

 

Approach:

The project examined the relationships between CT measured muscle density, IMF and MQ using analyses of data from previous projects.

Once these relationships were established, a larger dataset was used to estimate genetic parameters for CT measured MQ traits in live lambs.

 

Results:

The results from this study show that not only is it possible to accurately estimate intramuscular fat in the loin of Texel sheep using CT, but also that the methods developed in this study are transferable across different breed types. The results also show that intramuscular fat predicted by CT is clearly heritable, partially independent of overall fatness and has the potential to be included in current breeding programmes. These findings can now be used to develop breeding programmes enabling breeders to make the best use of modern technology to improve carcase quality whilst simultaneously at least maintaining or possibly even improving aspects of meat (eating) quality using corresponding weighting factors in the index construction.

 

 

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