Animal welfare assessment at group level is a scientific discipline that is rapidly developing. The interest in welfare assessment systems is based on an ethical concern for the welfare of farm animals. The scientific community plays an important role in delivering an appropriate repeatable, valid and feasible framework for these assessments. Consideration of the potential applications of these techniques is important for deciding upon the requirements of specific assessment systems. This paper provides a brief overview of the different types of applications, which can be categorised broadly into research, legislative requirements (non-voluntary), certification systems (voluntary) and advisory/management tools. These applications may have various goals: quantification of welfare, provision of welfare assurance or welfare management. Assessment systems vary in many characteristics, such as whether they are animal- or resource-based, and whether they are based on single or integrated scores. Different applications will require different elements of these features.

Given the absence of a ‘Golden Standard’ for the objective determination of welfare, the collection and interpretation of data involving different parameters is essential for assessing the well-being of farm animals. The choice of parameters and the relative weights assigned to each of them are crucial for the outcome of the assessment. Both elements involve a certain degree of subjectivity. In this paper we discuss the basics of different methods used to integrate welfare parameters, focussing on the issue of scientific objectivity. We begin by addressing parameter selection, the assignment of parameter weightings or rankings and the qualifications necessary for ‘experts’ designing and applying the methodology. Five different approaches to integrating parameters are then discussed. The paper does not state a preference for any method, but aims to encourage discussion of key elements involved with the on-farm assessment of welfare.

Developmental instability, of which fluctuating asymmetry is the most commonly used and recommended measure, has recently been claimed to be an objective, integrated and retrospective indicator of animal welfare. The theoretical and empirical grounds for these claims are reviewed. In theory, carefully selected composite indices of fluctuating asymmetry are valid indicators of animal welfare in the sense that they reflect the ability of the developmental processes of an animal, with a given genetic constitution, to cope with environmental stressors. Relevant scientific experiments are scant and are mainly restricted to poultry, but they are on the increase and they largely support the application of developmental instability for assessing animal welfare. A scheme for monitoring farm animal welfare based purely on measures of developmental instability would have important advantages, but cannot be recommended yet. It cannot be ruled out that certain factors are clearly relevant to the welfare status of an animal but do not notably/proportionally affect its morphogenesis. Moreover, such a monitoring scheme would not be appropriate for applications with an emphasis on problem analysis/management.

Qualitative interviews of farmers were carried out as part of a project focusing on developing animal welfare assessment systems (AWASs) in dairy, pig and mink production systems (26 farms in total). The aims of the interviews were to investigate farmers’ perceptions and experience of how an AWAS worked, and to explore their expectations for future use of AWASs. All interviews were taped, transcribed and analysed using a grounded-theory approach. The importance of different elements of the AWAS differed between farmers, and between farmers and the AWAS project implementation group. More direct associations between welfare assessment and production results (and other ‘common measures ‘) were requested by farmers. The whole AWAS ‘package’ was viewed as being too complex and expensive for most farmers, particularly as it involved multiple assessments over time. A range of themes emerged from the analysis. One of these, here referred to as ‘us and them’, is explored and discussed in this paper. Farmers were concerned that the AWAS could be used negatively in relation to consumers and political decisions, and they underlined that if the AWAS was to be used as a decision support tool (ie a system to assist them in making decisions about improvements in their herds and to guide their initiatives and improvements), it should include dialogue and details of local farm conditions. Qualitative interviews were found to provide valuable insight into farmers’ perceptions and expectations of animal welfare assessment methods.

As part of a larger on-farm dairy cow welfare and behaviour project, data were collected from 22 commercial dairy farms over two winters (2000-2001 and 2001-2002). A further winter of farm sampling will complete the project (2002-2003), with five types of housing and production systems being assessed: high-, medium- and low-milk-production herds with cubicle housing, high-production herds with zero grazing and cubicle housing, and medium-production herds with straw courts. All cows in one early or mid-lactation group from each farm were observed. For the current analysis, locomotion, cleanliness and body condition were scored for the group, and an audit of building quality was carried out. Analysis of the available data shows that some aspects of building design affect the welfare of dairy cows. A positive correlation was found between mean body condition score of the cows and mean locomotion score (P = 0.047). Body condition score correlated negatively with the number of cows in the group (P = 0.049). Negative correlations were found between locomotion score and the ratio of cubicles to cows (P = 0.033) and between the size of cubicles and leg cleanliness (P = 0.012). Trends were also seen in the relationships between farm type and locomotion score (P = 0.048), production level and locomotion score (P = 0.074) and cow cleanliness and cubicle size (P = 0.061). These results indicate that the quality of the housing and the management system can affect cow welfare. These measures may be useful to include in on-farm welfare assessment schemes.