Win–win strategies for high beef quality, consumer satisfaction, and farm efficiency, low environmental impacts and improved animal welfare

摘要

Meat quality includes intrinsic qualities (the characteristics of the product itself) and extrinsic qualities (e.g. animal health and welfare, environmental impacts, price). There is still a high level of variability in beef palatability, which induces consumer dissatisfaction. We also observe a general trend towards an increasing importance of healthiness and safety (intrinsic) and environmental issues and animal welfare (extrinsic). Most grading systems describe carcasses using only animal traits (e.g. weight, conformation, fatness, animal age and sex). In North American and Asian countries, emphasis has been put on maturity and marbling. The European system is mainly based on yield estimation. The Meat Standards Australia grading scheme, which predicts beef palatability for each cut, proved to be effective in predicting beef palatability in many other countries. Some genetic markers are available to improve beef quality. In addition, gene and protein expression profiling of the bovine muscle revealed that the expression level of many genes and the abundance of many proteins may be potential indicators of muscle mass, tenderness, flavour or marbling of meat. The integration of all these parameters is likely to predict better beef palatability. The integration of extrinsic qualities in the prediction model increases the difficulty of achieving a global evaluation of overall meat quality. For instance, with respect to environmental issues, each feeding system has its own advantages and disadvantages. Despite this, win–win strategies have been identified. For example, animals that were less stressed at slaughter also produced more tender meat, and in some studies the most economically efficient farms had the lowest environmental impact. In other cases, there are trade-offs among and between intrinsic and extrinsic qualities. In any case, the combination of the different integrative approaches appears promising to improve the prediction of overall beef quality. A relevant combination of indicators related to sensory and nutritional quality, social and environmental considerations (such as e.g. carbon footprint, animal welfare, grassland biodiversity, rural development) and economic efficiency (income of farmers and of other stakeholders of the supply chain, etc.) will allow the prediction of the overall quality of beef mainly for consumers but also for any stakeholder in the supply chain