Science forms a vital part of animal welfare assessment. However, many animal welfare issues are more influenced by public perception and political pressure than they are by science. The discipline of epidemiology has had an important role to play in examining the effects that management, environment and infrastructure have on animal-based measures of welfare. Standard multifactorial analyses have been used to investigate the effects of these various inputs on outcomes such as lameness. Such research has thereby established estimates of the probability of occurrence of these adverse welfare outcomes (AWOs) and given exposure to particular management inputs (welfare challenges). Welfare science has established various measures of the consequences of challenges to welfare. In this paper, a method is proposed for comparing the likely impact of different welfare challenges, incorporating both the probability of AWOs resulting from that welfare challenge, and their impacts or consequences if they do, using risk assessment principles. The rationale of this framework is explained. Its scope lies within a science-based risk assessment framework. This method does not provide objective measures or score of welfare without some context of comparison and does not provide new welfare measures but only provides a framework enabling objective comparison. Possible applications of this method include comparing the effects of specific management inputs, assigning priority to welfare challenges in order to inform allocation of resources for addressing those challenges, and comparisons of the lifetime welfare effects of management inputs or systems. The use of risk assessment methods in the animal welfare field can facilitate objective comparisons of situations that are currently assessed with some level of subjectivity. This methodology will require significant validation to determine its most productive use. The risk assessment approach could have a productive role in advancing quantitative assessment in animal welfare science.

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The aim of this paper was to present an alternative multi-criteria evaluation model to assess animal welfare on farms based on the Welfare Quality ® (WQ) project, using an example of welfare assessment of growing pigs. The WQ assessment protocol follows a three-step aggregation process. Measures are aggregated into criteria, criteria into principles and principles into an overall assessment. This study focussed on the first step of the aggregation. Multi-attribute utility theory (MAUT) was used to produce a value of welfare for each criterion. The utility functions and the aggregation function were constructed in two separated steps. The Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) method was used for utility function determination and the Choquet Integral (CI) was used as an aggregation operator. The WQ decision-makers' preferences were fitted in order to construct the utility functions and to determine the CI parameters. The methods were tested with generated data sets for farms of growing pigs. Using the MAUT, similar results were obtained to the ones obtained applying the WQ protocol aggregation methods. It can be concluded that due to the use of an interactive approach such as MACBETH, this alternative methodology is more transparent and more flexible than the methodology proposed by WQ, which allows the possibility to modify the model according, for instance, to new scientific knowledge.

The aim of this paper was to validate an alternative multi-criteria evaluation system to assess animal welfare on farms based on the Welfare Quality ® (WQ) project, using an example of welfare assessment of growing pigs. This alternative methodology aimed to be more transparent for stakeholders and more flexible than the methodology proposed by WQ. The WQ assessment protocol for growing pigs was implemented to collect data in different farms in Schleswig-Holstein, Germany. In total, 44 observations were carried out. The aggregation system proposed in the WQ protocol follows a three-step aggregation process. Measures are aggregated into criteria, criteria into principles and principles into an overall assessment. This study focussed on the first two steps of the aggregation. Multi-attribute utility theory (MAUT) was used to produce a value of welfare for each criterion and principle. The utility functions and the aggregation function were constructed in two separated steps. The MACBETH (Measuring Attractiveness by a Categorical-Based Evaluation Technique) method was used for utility function determination and the Choquet integral (CI) was used as an aggregation operator. The WQ decision-makers' preferences were fitted in order to construct the utility functions and to determine the CI parameters. The validation of the MAUT model was divided into two steps, first, the results of the model were compared with the results of the WQ project at criteria and principle level, and second, a sensitivity analysis of our model was carried out to demonstrate the relative importance of welfare measures in the different steps of the multi-criteria aggregation process. Using the MAUT, similar results were obtained to those obtained when applying the WQ protocol aggregation methods, both at criteria and principle level. Thus, this model could be implemented to produce an overall assessment of animal welfare in the context of the WQ protocol for growing pigs. Furthermore, this methodology could also be used as a framework in order to produce an overall assessment of welfare for other livestock species. Two main findings are obtained from the sensitivity analysis, first, a limited number of measures had a strong influence on improving or worsening the level of welfare at criteria level and second, the MAUT model was not very sensitive to an improvement in or a worsening of single welfare measures at principle level. The use of weighted sums and the conversion of disease measures into ordinal scores should be reconsidered.

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