Review: Connecting circularity to animal welfare calls for a 'novel' conceptual framework based on integrity.

The current food system is not sustainable. Circular agriculture aims to save the environment and produce food sustainably by closing nutrient cycles, possibly without improving animal welfare. This paper proposes a new conceptual framework, called a circular welfare economy (CWE), to facilitate a transition towards a sustainable agriculture based on integrity. The CWE framework explains how welfare relates to circular agriculture, how potential conflicts can be solved and what future livestock farming could look like. CWE applies the notion of circularity to welfare defined as the quality of life as perceived by the individual itself. CWE also identifies human integrity, defined as being open and honest, as a sine qua non for sustainability. Animal-welfare problems arise when animals are merely used as a means, e.g., for profits. Instead, profits and circular agriculture are means to the end of welfare. In a CWE, welfare is promoted sustainably, without causing undue need frustration in other individuals. This requires informed moral decision-making involving human integrity and the closure of welfare-related feedback loops. Conflicts between circular agriculture and animal welfare are solved by weighing all welfare needs impartially. Three future scenarios are presented: Animal-welfare-exclusive circular agriculture, which resembles modern intensive livestock farming, animal rights agriculture without livestock farming, and a CWE-based agriculture which integrates circular agriculture and animal welfare. In the latter case, we will not use animals merely as a means to close nutrient cycles, but take every effort, openly and honestly, to understand and benefit their points of view as we do our own.

Individual piglets' contribution to the development of tail biting.

Conflicting hypotheses exist about the contribution of individual pigs to the development of a tail-biting outbreak, but there is limited quantitative information to support or dismiss them. This study aims to quantify the development of tail-biting behaviour at pen and individual piglet level, before and after the first visible tail damage. Video recordings of 14 pens with tail-biting outbreaks and individually marked weaned piglets were used to observe tail-biting incidents (TBIs; piglet biting a penmate's tail). When visible tail damage was first observed in a pen (i.e. day of tail biting outbreak; D0), the video recordings of the previous 6 (till D-6) and the following 6 days (till D6) were analysed every other day for TBIs and the identities of the biter and bitten piglet were recorded. The average TBIs per individual piglet (within each pen) per observation day were analysed to quantify the development of tail-biting behaviour and to identify pronounced biters and/or bitten piglets. The (absence of) coherence for TBIs in a pen was used to test whether biters preferred a specific penmate. There was an exponential increase in the intensity (linear on log scale) of the TBIs from an average of 0.7 bites/h at D-6 to 2.3 bites/h at D6. An additional negative quadratic component suggests that a plateau for tail-biting behaviour was reached by the end of the observation period. Before any visible tail damage was observed (i.e. before D0), 82% of the piglets performed and 96% of them received tail bites. After D0, the figures were 99% and 100%, respectively. One or a few pronounced biters could be identified in almost all pens. These biters already showed more tail biting at D-6 than their penmates. Furthermore, these biters showed a greater increase in tail-biting behaviour during the observation period than the average scores of their penmates. In contrast, there was no apparent increase in the receipt of bites among the piglets that had already been bitten more than their penmates at D-6. Finally, there was no significant coherence between biters and bitten piglets, indicating that biters showed no preference for biting particular penmates, even when some of them had a damaged tail. These results show that, by using observations of TBIs, possible biters or bitten piglets can already be identified 6 days before tail damage is first apparent in a pen.

Characteristics of biter and victim piglets apparent before a tail-biting outbreak.

Little is known about the characteristics of biters and victims before the appearance of a tail-biting outbreak in groups of pigs. This study aimed to characterise biters and victims (according to gender and performance) and to quantify their behavioural development during the 6 days preceding the tail-biting outbreak. The hypotheses tested were: (a) biters are more often female, are the lighter pigs in the group, are more restless and perform more aggressive behaviour; and (b) victims are more often male, heavier and less active. Using video recordings we carried out a detailed study of 14 pens with a tail-biting outbreak among the weaned piglets. All piglets were individually marked and we observed the behaviour of biters, victims and control piglets (piglet types). In every pen, each piglet type was observed every other day from 6 days before (D-6) to the day of the first visible tail damage (i.e. day of tail biting outbreak; D0). While the number of male biters (6 of the 14 biters) and male victims (11 of the 14 victims) was not significantly different (P = 0.13), this numerical contrast was considerable. The start weight of victims was significantly (P = 0.03) higher (8.6 kg) than those of biters (7.5 kg) and control piglets (8.0 kg). Biters tended (P = 0.08) to spend longer sitting/kneeling (3.1 min/h) than controls (1.7 min/h), but no differences were seen in the time spent lying or standing. Victims tended (P = 0.07) to change posture more often (restlessness) than controls and chased penmates more (P = 0.04) than biters. Victims also performed more (P = 0.04) aggressive behaviour than biters and controls. In contrast, biters tended (P = 0.08) to be chased by penmates more often and tended (P = 0.06) to receive more aggressive behaviour than controls. Furthermore, biters spent longer manipulating the enrichment device (P = 0.01) and the posterior/tail (P = 0.02) of their penmates than controls and tended (P = 0.06) to perform more tail bites than victims. Victims received more posterior/tail manipulation (P = 0.02) and tail bites (P = 0.04) than controls. It was also noticed that, independent of piglet type, restlessness (P = 0.03) increased and the frequency of performed tail bites tended (P = 0.08) to increase in the 6 days preceding a tail-biting outbreak. These findings may contribute to the early identification of biters or victims and support the development of strategies to minimise the occurrence of tail biting.

Synthesis of semantic modelling and risk analysis methodology applied to animal welfare.

Decision-making on animal welfare issues requires a synthesis of information. For the assessment of farm animal welfare based on scientific information collected in a database, a methodology called 'semantic modelling' has been developed. To date, however, this methodology has not been generally applied. Recently, a qualitative Risk Assessment approach has been published by the European Food Safety Authority (EFSA) for the first time, concerning the welfare of intensively reared calves. This paper reports on a critical analysis of this Risk Assessment (RA) approach from a semantic-modelling (SM) perspective, emphasizing the importance of several seemingly self-evident principles, including the definition of concepts, application of explicit methodological procedures and specification of how underlying values and scientific information lead to the RA output. In addition, the need to include positive aspects of welfare and overall welfare assessments are emphasized. The analysis shows that the RA approach for animal welfare could benefit from SM methodology to support transparent and science-based decision-making.

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.

Aggregation of measures to produce an overall assessment of animal welfare. Part 2: analysis of constraints.

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® 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.

Decision support system for overall welfare assessment in pregnant sows A: model structure and weighting procedure.

The problem of how to objectively assess the overall welfare status of animals under farming conditions has contributed to an ongoing debate that has hampered actual decision making on animal welfare. For this reason we constructed a model based on the assumed hierarchical organization of the animals' needs for overall welfare assessment in the case of pregnant sows. This model is implemented in a computer-based decision support system that takes a description of a housing and management system as input and produces a welfare score as output. A formalized procedure was used to construct the model for welfare assessment in pregnant sows on the basis of available scientific knowledge. This SOWEL (from SOw WELfare) model contains 37 attributes that describe the welfare-relevant properties of housing and management systems. In the decision support system these attributes are linked to scientific statements and a list of needs to provide a scientific basis for welfare assessment. Weighting factors that represent the relative importance of the attributes are derived from the scientific statements about the various welfare performance criteria that have been measured by scientists. The welfare score is calculated as the weighted average score. All information in the decision support system is stored in tables in a relational database such that newly available knowledge and insights can be incorporated to refine the model. The model has been developed in line with several existing models but it differs from these models in that it is the first to provide a formalized procedure to explicate the reasoning steps involved in welfare assessment based on available scientific knowledge.

Decision support system for overall welfare assessment in pregnant sows B: validation by expert opinion.

This paper examines the validity of a model that is embedded in a computer-based decision support system to assess the welfare status of pregnant sows in housing and management systems. The so-called SOWEL (SOw WELfare) model was constructed using a formalized procedure to identify and weight welfare-relevant attributes of housing systems in relation to the animal's needs, and evidenced by scientific statements collected in a database. The model's predictions about welfare scores for 15 different housing systems and weighting factors for 20 attributes were compared with expert opinion, which was solicited using a written questionnaire for pig-welfare scientists. The experts identified tethering and individual housing in stalls as low welfare systems. The group of midwelfare systems contained indoor group-housing systems and an individual-housing system with additional space and substrate. The five best systems were all systems with outdoor access and the provision of some kind of substrate such as straw. The highest weighting factors were given for the attributes "social contact," "health and hygiene status," "water availability," "space per pen," "foraging and bulk," "food agonism," "rooting substrate," "social stability," and "movement comfort." The degree of concordance among the experts was reasonable for welfare scores of housing systems, but low for weighting factors of attributes. Both for welfare scores and weighting factors the model correlated significantly with expert opinion (Spearman's Rho: 0.92, P < 0.001, and 0.72, P < 0.01, respectively). The results support the validity of the model and its underlying procedure to assess farm-animal welfare in an explicit and systematic way based on available scientific knowledge.