The Welfare Quality multi-criteria evaluation (WQ-ME) model aggregates scores of single welfare measures into an overall assessment for the level of animal welfare in dairy herds. It assigns herds to 4 welfare classes: unacceptable, acceptable, enhanced, or excellent. The aim of this study was to demonstrate the relative importance of single welfare measures for WQ-ME classification of a selected sample of Dutch dairy herds. Seven trained observers quantified 63 welfare measures of the Welfare Quality protocol in 183 loose housed- and 13 tethered Dutch dairy herds (herd size: 10 to 211 cows). First, values of welfare measures were compared among the 4 welfare classes, using Kruskal-Wallis and Chi-squared tests. Second, observed values of single welfare measures were replaced with a fictitious value, which was the median value of herds classified in the next highest class, to see if improvement of a single measure would enable a herd to reach a higher class. Sixteen herds were classified as unacceptable, 85 as acceptable, 78 as enhanced, and none as excellent. Classification could not be calculated for 17 herds because data were missing (15 herds) or data were deemed invalid because the stockperson disturbed behavioral observations (2 herds). Herds classified as unacceptable showed significantly more very lean cows, more severely lame cows, and more often an insufficient number of drinkers than herds classified as acceptable. Herds classified as acceptable showed significantly more cows with high somatic cell count, with lesions, that could not be approached closer than 1m, colliding with components of the stall while lying down, and lying outside the lying area, and showed fewer cows with diarrhea, more often had an insufficient number of drinkers, and scored lower for the descriptors "relaxed" and "happy" than herds classified as enhanced. Increasing the number of drinkers and reducing the percentage of cows colliding with components of the stall while lying down were the changes most effective in allowing herds classified as unacceptable and acceptable, respectively, to reach a higher class. The WQ-ME model was not very sensitive to improving single measures of good health. We concluded that a limited number of welfare measures had a strong influence on classification of dairy herds. Classification of herds based on the WQ-ME model in its current form might lead to a focus on improving these specific measures and divert attention from improving other welfare measures. The role of expert opinion and the type of algorithmic operator used in this model should be reconsidered.

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.

The overall assessment of animal welfare is a multicriterion evaluation problem that needs a constructive strategy to compound information produced by many measures. The construction depends on specific features such as the concept of welfare, the measures used and the way data are collected. Welfare is multidimensional and one dimension probably cannot fully compensate for another one (e.g. good health cannot fully compensate for behavioural deprivation). Welfare measures may vary in precision, relevance and their relative contribution to an overall welfare assessment. The data collected are often expressed on ordinal scales, which limits the use of weighted sums to aggregate them. A sequential aggregation is proposed in the Welfare Quality R project, first from measures to welfare criteria (corresponding to dimensions with pre-set objectives) and then to an overall welfare assessment, using rules determined at each level depending on the nature and number of variables to be considered and the level of compensation to be permitted. Scientific evidence and expert opinion are used to refine the model, and stakeholders' approval of general principles is sought. This approach could potentially be extended to other problems in agriculture such as the overall assessment of the sustainability of production systems.

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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.

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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.

Welfare is multidimensional, comprising good health, comfort, expression of behaviour, and so on. Its overall assessment therefore requires a multicriteria evaluation. The set of criteria shall be exhaustive (no missing item), minimal (only necessary items), agreed by stakeholders, and legible (a limited number of criteria). Furthermore, the interpretation from one criterion shall not depend on that from another. We propose a set of 12 subcriteria grouped into four criteria: feeding, housing, health and optimised emotional states. This work will assist in developing measures to be used on-farm to form a European standard for overall assessment of animal welfare.