Raphaëlle Botreau

Opportunities for predicting and manipulating beef quality.

Meat quality is a complex concept and can be defined as the characteristics of meat which satisfy consumers and citizens. The quality concept can be divided into intrinsic quality traits (which are the characteristics of the product itself) and extrinsic quality traits (which are more or less associated to the product for instance the price, a major determinant of purchase, or any brand or quality label). Quality can also be generic for the mass market or specific for niche markets. The relative importance of the different quality traits varies with human culture and time with a general trend of an increasing contribution of healthiness, safety and extrinsic quality traits. This review underlines the need for the development of methods to interpret and aggregate measures under specific rules to be defined in order to produce an overall assessment of beef quality. Such methods can be inferred for example from genomic results or data related to muscle biochemistry to better predict tenderness or flavor. A more global assurance quality scheme (the Meat Standards Australia System) based on the aggregation of sensory quality traits has been developed in Australia to ensure palatability to consumers. We speculated that the combination of indices related to sensory and nutritional quality, social and environmental considerations (carbon footprint, animal welfare, biodiversity of pasture, rural development, etc.) and economic efficiency (incomes of farmers and of others players along the supply chain, etc.) will provide objective assessment of the overall quality of beef (i.e. incorporating an all encompassing approach) not only for the mass market but also to support official quality labels of niche markets which are so far mainly associated with the geographical origins of the products.

Aggregation of measures to produce an overall assessment of animal welfare. Part 1: a review of existing methods.

Several systems have been proposed for the overall assessment of animal welfare at the farm level for the purpose of advising farmers or assisting public decision-making. They are generally based on several measures compounded into a single evaluation, using different rules to assemble the information. Here we discuss the different methods used to aggregate welfare measures and their applicability to certification schemes involving welfare. Data obtained on a farm can be (i) analysed by an expert who draws an overall conclusion; (ii) compared with minimal requirements set for each measure; (iii) converted into ranks, which are then summed; or (iv) converted into values or scores compounded in a weighted sum (e.g. TGI35L) or using ad hoc rules. Existing methods used at present (at least when used exclusively) may be insufficiently sensitive or not routinely applicable, or may not reflect the multidimensional nature of welfare and the relative importance of various welfare measures. It is concluded that different methods may be used at different stages of the construction of an overall assessment of animal welfare, depending on the constraints imposed on the aggregation process.

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

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.

Towards an agroecological assessment of dairy systems: proposal for a set of criteria suited to mountain farming

Ruminant production systems have been facing the sustainability challenge, namely, how to maintain or even increase production while reducing their environmental footprint, and improving social acceptability. One currently discussed option is to encourage farmers to follow agroecological principles, that is, to take advantage of ecological processes to reduce inputs and farm wastes, while preserving natural resources, and using this diversity to increase system resilience. However, these principles need to be made more practical. Here, we present the procedure undertaken for the collaborative construction of an agroecological diagnostic grid for dairy systems with a focus on the mountain farming relying on the use of semi-natural grasslands. This diagnosis will necessarily rely on a multicriteria evaluation as agroecology is based on a series of complementary principles. It requires defining a set of criteria, based on practices to be recommended, that should be complied with to ensure agroecological production. We present how such agroecological criteria were identified and organized to form the architecture of an evaluation model. As a basis for this work, we used five agroecological principles already proposed for animal production systems. A group of five experts of mountain production systems and of their multicriteria evaluation was selected, with a second round of consultation with five additional experts. They first split up each principle into three to four generic sub-principles. For each principle, they listed three to eight categories of state variables on which the fulfilment of the principle should have a positive impact (e.g. main health disorders for the integrated health management principle). State variables are specific for a given production, for example, dairy farms. Crossing principles with state variables enabled experts to build five matrices, with 75 cells relevant for dairy systems. In each cell, criteria are specific to the local context, for example, mountain dairy systems in this study. Finally, we discuss the opportunities offered by our methodology, and the steps remaining for the construction of the evaluation model.

Integration of data collected on farms or at slaughter to generate an overall assessment of animal welfare

One objective of the WelfareQuality® project was to propose a standardised assessment method that could be used to provide transparent information on farm animal welfare to all relevant stakeholders. As described in earlier chapters, Welfare Quality® therefore built welfare assessment systems for cattle, pigs and poultry incorporating numerous measures based preferably on the animals but also to a lesser extent on resources and management of animal units (farms or slaughter plants). Of course, the substantial amount of data gathered during assessment needs to be meaningfully interpreted in terms of welfare and then integrated to provide an overall evaluation of the animal unit. Therefore, Welfare Quality® designed a scoring model to integrate the data and to translate value judgements into refined and easily understandable information that could serve various purposes and guide the decisions of stakeholders (including consumers) with regard to farm animal welfare.