Development of a multi-criteria evaluation system to assess growing pig welfare

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.     

Assessment of the multi-criteria evaluation system of the Welfare Quality® protocol for growing pigs

Animal welfare has become an important subject of public and political debate, leading to the necessity of an objective evaluation system for on-farm use. As welfare is a multi-dimensional concept, it makes sense to use a multi-criteria aggregation system to obtain an overall welfare score. Such an aggregation system is provided by the Welfare Quality® Network. The present paper focusses on the assessment of the multi-criteria evaluation model included in the Welfare Quality® protocol for growing pigs in order to aggregate the animal-based indicators first to criteria, then to principles and finally to an overall welfare score. Specifically, the importance of the indicators on the overall assessment of growing pig farms is analysed in a given population which consisted of a total of 198 protocol assessments carried out on a sample of 24 farms in Germany. By means of partial least squares modelling, the influence of measures in the calculation procedure is estimated by calculation and interpretation of Variable Importance for Projection (VIP) scores. Variable Importance for Projection scores revealed some meaningful, unexpected influences as the multi-criteria evaluation model of Welfare Quality® aimed at avoiding interferences and double-counting. Some of these influences led to the assumption that some measures might have potential as iceberg indicators, whereas others showed lesser importance. Thus, feasibility can be gained by the deletion and special weighting of indicators according to their importance. Altogether, the study is an essential contribution to the further development of the Welfare Quality® protocols as well as the application of multi-criteria decision systems in the field of animal welfare science in general.     

On-farm animal welfare assessment in beef bulls: consistency over time of single measures and aggregated Welfare Quality® scores

Consistency over time of (on-farm) animal welfare assessment systems forms part of reliability, meaning that results of the assessment should be representative of the longer-term welfare state of the farm as long as the housing and management conditions have not changed considerably. This is especially important if assessments are to be used for certification purposes. It was the aim of the present study to investigate consistency over time of the Welfare Quality® (WQ®) assessment system for fattening cattle at single measure level, aggregated criterion and principle scores, and overall classification across short-term (1 month) and longer-term periods (6 months). We hypothesized that consistency over time of aggregated criterion and principle scores is higher than that of single measures. Consistency was also expected to be lower with longer intervals between assessments. Data were obtained using the WQ® protocol for fattening cattle during three visits (months 0, 1 and 7) on 63 beef farms in Austria, Germany and Italy. Only data from farms where no major changes in housing and management had taken place were considered for analysis. At the single measure level, Spearman rank correlations between visits were >0.7 and variance was lower within farms than between farms for six and two of 19 measures after 1 month and 6 months, respectively. After aggregation of single measures into criterion and principle scores, five and two of 10 criteria and three and one of four principles were found reliable after 1 and 6 months, respectively. At the WQ® principle level, this was the case for three and one of four principles. Seventy-nine per cent and 75% of the farms were allocated to the same overall welfare category after 1 month and 6 months. Possible reasons for a lack of consistency are seasonal effects or short-term fluctuations that occur under normal farm conditions, low prevalence of clinical measures and probably insufficient sample size, whereas poor inter-observer agreement leading to inflation of correlation can be ruled out. At the criterion and principle level, aggregation of information into scores appears to partly smoothen undirected variation at the single measure level without losing sensitivity in terms of welfare evaluation. Reliable on-farm animal welfare assessments should therefore be based on repeated assessments. Further long-term studies are recommended to better understand the factors influencing consistency over time.     

Sensitivity of the Welfare Quality® broiler chicken protocol to differences between intensively reared indoor flocks: which factors explain overall classification?

There is a large demand for holistic welfare assessment systems that result in a singular balanced summary of welfare. The Welfare Quality® (WQ) broiler protocol summarizes 18 welfare measures into four principles (‘good feeding’, ‘good housing’, ‘good health’ and ‘appropriate behaviour’), which are then integrated into one overall category (‘excellent’, ‘enhanced’, ‘acceptable’ or ‘not classified’). But the protocol is time consuming which hampers implementation. Furthermore, WQ’s aim to assess animal welfare in a wide range of husbandry systems may decrease its ability to discriminate between flocks from the same system. We applied the protocol in the context of intensive indoor rearing to assess whether it discriminated sufficiently between flocks, could be shortened without losing essential information, and provided a balanced summary of welfare. The vast majority of the flocks (88%) received the same overall classification (acceptable) whilst all other flocks received an adjacent classification (enhanced), suggesting poor discriminative capacity. For 95% of the flocks overall classification was explained by two measures only (‘drinker space’ and ‘stocking density’). A system based on these two measures would reduce assessment time from 3.5 h to a few minutes. However, both measures’ validity can be questioned as they are risk factors for poor welfare rather than animal-based outcome measures and they suffer from methodological weaknesses. Furthermore, the possibility for such an extreme simplification raises doubts on whether the overall classification reflects a balanced summary of different welfare aspects. In line with this, overall classification was not affected by replacing single measures within the ‘good health’ and ‘appropriate behaviour’ principles with realistically attainable minima or maxima for intensively reared flocks. Even replacing either of these two principles entirely with their realistically obtainable minimum or maximum did not affect classification. Such insensitivity to change may discourage attempts to improve the welfare of intensively reared flocks when assessments are made based on the overall classification. This calls for an adjustment of the classification system, which is currently being developed by the Welfare Quality Network.     

Application of the Welfare Quality® assessment system on European beef bull farms

Welfare concerns for intensive beef production have often been raised, but on-farm welfare assessment studies are rare. The aim of this study was to apply the Welfare Quality® (WQ) welfare assessment system for fattening cattle on beef bull farms to evaluate the state of welfare at the level of WQ measures and of aggregated scores, as well as overall classification. In addition, the purpose was to evaluate two ways of providing feedback information to the farmers with regard to possible welfare improvements on the farms. The study was conducted in Austria, Germany and Italy on a total of 63 beef bull farms with deep litter or cubicle-housing systems. Assessments were carried out 3 times (1 month and 7 months apart from the initial visit). In every country, farmers were assigned to two treatment groups (feedback from initial visit as written report, F, written feedback plus oral advice, FA) and a control group (C), which did not receive any feedback. At the criterion level, the highest average welfare scores were obtained from ‘Absence of prolonged hunger’ (94/100 points) followed by ‘Absence of pain induced by management procedures’ (88/100) and ‘Comfort around resting’ (77/100). Most welfare concerns related to the criteria ‘Absence of disease’ (40/100), ‘Expression of social behaviour’ (44/100) and ‘Positive emotional state’ (48/100), thus indicating room for improvements. Two-thirds of the farms achieved the ‘Enhanced’ level, about one-third was judged ‘Acceptable’ and only one farm ‘Excellent’. After 6 months of monitoring period, there was no significant welfare improvement in both the treatment groups as compared with the control group. Reasons for the lack of effect may mainly be seen in the short monitoring period and a lack of external incentives. In conclusion, the WQ assessment system revealed areas for improvement, but longer term studies and investigations on alternative ways of transferring outcomes from on-farm welfare assessments to farmers should be carried out in future.     

Simplifying the Welfare Quality® assessment protocol for broiler chicken welfare

Welfare Quality® (WQ) assessment protocols place the emphasis on animal-based measures as an indicator for animal welfare. Stakeholders, however, emphasize that a reduction in the time taken to complete the protocol is essential to improve practical applicability. We studied the potential for reduction in time to complete the WQ broiler assessment protocol and present some modifications to the protocol correcting a few errors in the original calculations. Data was used from 180 flocks assessed on-farm and 150 flocks assessed at the slaughter plant. Correlations between variables were calculated, and where correlation was moderate, meaningful and promising (in terms of time reduction), simplification was considered using one variable predicted from another variable. Correlation analysis revealed a promising correlation between severe hock burn and gait scores on-farm. Therefore, prediction of gait scores using hock burn scores was studied further as a possible simplification strategy (strategy 1). Measurements of footpad dermatitis, hock burn, cleanliness and gait score on-farm correlated moderately to highly with slaughter plant measurements of footpad dermatitis and/or hock burn, supporting substitution of on-farm measurements with slaughter plant data. A simplification analysis was performed using footpad dermatitis, hock burn, cleanliness and gait scores measured on-farm predicted from slaughter plant measurements of footpad dermatitis and hock burn (strategy 2). Simplification strategies were compared with the full assessment protocol. Close agreement was found between the full protocol and both simplification strategies although large confidence intervals were found for specificity of the simplified models. It is concluded that the proposed simplification strategies are encouraging; strategy 1 can reduce the time to complete the on-farm assessment by ~1 h (25% to 33% reduction) and strategy 2 can reduce on-farm assessment time by ~2 h (50% to 67% reduction). Both simplification strategies should, however, be validated further, and tested on farms with a wide distribution across the different welfare categories of WQ.     

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.     

Assessment of vulnerability to climate change using a multi-criteria outranking approach with application to heat stress in Sydney

Climate change vulnerability assessment is a complex form of risk assessment which accounts for both geophysical and socio-economic components of risk. In indicator-based vulnerability assessment (IBVA), indicators are used to rank the vulnerabilities of socio-ecological systems (SESs). The predominant aggregation approach in the literature, sometimes based on multi-attribute utility theory (MAUT), typically builds a global-scale, utility function based on weighted summation, to generate rankings. However, the corresponding requirement for additive independence and complete knowledge of system interactions by analyst are rarely if ever satisfied in IBVA.We build an analogy between the structures of Multi-Criteria Decision Analysis (MCDA) and IBVA problems and show that a set of techniques called Outranking Methods, developed in MCDA to deal with criteria incommensurability, data uncertainty and preference imprecision, offer IBVA a sound alternative to additive or multiplicative aggregation. We reformulate IBVA problems within an outranking framework, define thresholds of difference and use an outranking method, ELECTRE III, to assess the relative vulnerability to heat stress of 15 local government areas in metropolitan Sydney. We find that the ranking outcomes are robust and argue that an outranking approach is better suited for assessments characterized by a mix of qualitative, semi-quantitative and quantitative indicators, threshold effects and uncertainties about the exact relationships between indicators and vulnerability.     

Navigating the iceberg: reducing the number of parameters within the Welfare Quality® assessment protocol for dairy cows

The Welfare Quality® protocols provide a multidimensional assessment of welfare, which is lengthy, and hence limited in terms of practicality. The aim of this study was to investigate potential ‘iceberg indicators’ which could reliably predict the overall classification as a means of reducing the length of time for an assessment and so increase the feasibility of the Welfare Quality® protocol as a multidimensional assessment of welfare. Full Welfare Quality® assessments were carried out on 92 dairy farms in England and Wales. The farms were all classified as Acceptable or Enhanced. Logistic regression models with cross validation were used to compare model fit for the overall classification on farms. ‘Absence of prolonged thirst’, on its own, was found to correctly classify farms 88% of the time. More generally, the inclusion of more measures in the models was not associated with greater predictive ability for the overall classification. Absence of prolonged thirst could thus, in theory, be considered to be an iceberg indicator for the Welfare Quality® protocol, and could reduce the length of time for a farm assessment to 15 min. Previous work has shown that the parameters within the Welfare Quality® protocol are important and relevant for welfare assessment. However, it is argued that the credibility of the published aggregation system is compromised by the finding that one resource measure (Absence of prolonged thirst) is a major driver for the overall classification. It is therefore suggested that the prominence of Absence of prolonged thirst in this role may be better understood as an unintended consequence of the published measure aggregation system rather than as reflecting a realistic iceberg indicator.     

The ‘Real Welfare’ scheme: benchmarking welfare outcomes for commercially farmed pigs

Animal welfare standards have been incorporated in EU legislation and in farm assurance schemes, based on scientific information and aiming to safeguard the welfare of the species concerned. Recently, emphasis has shifted from resource-based measures of welfare to animal-based measures, which are considered to assess more accurately the welfare status. The data used in this analysis were collected from April 2013 to May 2016 through the ‘Real Welfare’ scheme in order to assess on-farm pig welfare, as required for those finishing pigs under the UK Red Tractor Assurance scheme. The assessment involved five main measures (percentage of pigs requiring hospitalization, percentage of lame pigs, percentage of pigs with severe tail lesions, percentage of pigs with severe body marks and enrichment use ratio) and optional secondary measures (percentage of pigs with mild tail lesions, percentage of pigs with dirty tails, percentage of pigs with mild body marks, percentage of pigs with dirty bodies), with associated information about the environment and the enrichment in the farms. For the complete database, a sample of pens was assessed from 1928 farm units. Repeated measures were taken in the same farm unit over time, giving 112 240 records at pen level. These concerned a total of 13 480 289 pigs present on the farm during the assessments, with 5 463 348 pigs directly assessed using the ‘Real Welfare’ protocol. The three most common enrichment types were straw, chain and plastic objects. The main substrate was straw which was present in 67.9% of the farms. Compared with 2013, a significant increase of pens with undocked-tail pigs, substrates and objects was observed over time (P<0.05). The upper quartile prevalence was <0.2% for all of the four main physical outcomes, and 15% for mild body marks. The percentage of pigs that would benefit from being in a hospital pen was positively correlated to the percentage of lame pigs, and the absence of tail lesions was positively correlated with the absence of body marks (P<0.05, R>0.3). The results from the first 3 years of the scheme demonstrate a reduction of the prevalence of animal-based measures of welfare problems and highlight the value of this initiative.     

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.     

A Decision Support Framework for Sustainable Waste Management

This article describes a decision support framework for the evaluation of scenarios for the integrated management of municipal solid waste within a local government area (LGA).
The work is initially focused on local government (i.e., municipal councils) in the state of Queensland, Australia; however, it is broadly applicable to LGAs anywhere. The goal is to achieve sustainable waste management practices by balancing global and regional environmental impacts, social impacts at the local community level, and economic impacts. The framework integrates life-cycle assessment (LCA) with other environmental, social, and economic tools. For this study, social and economic impacts are assumed to be similar across developed countries of the world. LCA was streamlined at both the life-cycle inventory and life-cycle impact assessment stages.
For this process, spatial resolution is introduced into the LCA process to account for impacts occurring at the local and regional levels. This has been done by considering social impacts on the local community and by use of a regional procedure for LCA data for emissions to the environment that may have impacts at the regional level.
The integration follows the structured approach of the pressure-state-response (PSR) model suggested by the Organisation for Economic Cooperation and Development (OECD). This PSR model has been extended to encompass nonenvironmental issues and to guide the process of applying multiple tools.
The framework primarily focuses on decision analysis and interpretation processes. Multiattribute utility theory (MAUT) is used to assist with the integration of qualitative and quantitative information. MAUT provides a well-structured approach to information assessment and facilitates objective, transparent decisions. A commercially available decision analysis software package based on MAUT has been used as the platform for the framework developed in this study.     

Multi-criteria evaluation of multi-purpose stand treatment programmes for Finnish boreal forests under changing climate

The aim of this study is determine the potential of multiple-criteria analysis (MCA) methodology to assess the utility of different multi-objective stand treatment programmes, in the boreal forest. Simulations over a planning period of 100 years under current and changing climate scenarios by a physiological growth model provided input data for the stochastic multi-criteria analysis of forest management alternatives. Expected utility with regard to a set of objectives was estimated with an additive utility model. Timber production, carbon sequestration and biodiversity were considered as partial objectives; these in term were represented by decision criteria. The weights of partial objectives and criteria were subjected to uncertainty due to missing exact stakeholder preference information. Generic probability distributions for individual weights were defined based on qualitative information. Alternative stand treatment programmes with regard to the decision criteria were evaluated by means of linear preference functions. In a Monte Carlo analysis the utility model was employed to evaluate eight stand treatment programmes for their performance regarding the management objectives. The effect of climate change on utility of stand treatment programmes was examined also as an important factor. Over two sites and three tree species just two out of eight stand treatment programmes analysed in this study were able to dominate the set of alternatives; namely the unthinned scenario and the highest increase in thinning thresholds scenario (UT(0, 0) and BT(+30, +30)) in which stocking was kept at higher level compared to the currently recommended guidelines for site and species. Reducing uncertainty in the utility model by assuming accurate weights for the decision criteria did not show large changes neither in expected utility nor in the ranking of alternatives. Ignoring necessary revisions of how the forest is to be managed in the future might potentially result in losses, not only for timber production but also for benefits such as carbon sequestration and other amenities including biodiversity.     

Co-construction of an assessment method of the environmental sustainability for cattle farms involved in a Protected Designation of Origin (PDO) cheese value chain,Cantal PDO

In France, numerous Protected Designation of Origin (PDO) cheese value chains have revised their specifications to address environmental protection, particularly at the farm level. In this stage, it is nevertheless impossible to state whether the proposed modifications may ensure the sustainability of environmental protection as no environmental sustainability diagnostic has been conducted. The aim of this study is to construct an assessment method for the Cantal PDO farms.A multi-criteria assessment approach was implemented in the following 5 steps: definition of the framework of the study, determination of the principles and criteria, definition of the indicators, aggregation of all elements of the assessment, and finally, test of the assessment method on farms. Focus groups meetings were organised for the first four steps. The panel discussions were based on data obtained from both the literature and surveys of stakeholders operating in the value chain and within the territory.The obtained assessment method comprises 4 principles characterised by 33 indicators. The most important principle (45%), management of grassland resources, is a key economic and environmental point of the studied farms. It reflects the necessary intensive use of grass by the animals in the PDO farms. Few existing methods addressed this principle that required the design of several new indicators. The second principle, impact of agricultural practices (25%), is often included in environmental sustainability assessment methods The adopted indicators are, thus, drawn from existing methods and adapted to the context of the farms studied. The management of the farm buildings and the landscape principle (18%) is often considered in social sustainability. Its role in environmental sustainability can be explained by its strong link with the image of PDO products. Primarily drawn from the literature, the used indicators have been extended to make the assessment method as objective as possible. Finally, the management of local, energy and water resources principle has the lowest weighting (12%). This can be explained by the limited number of indicators.This assessment method was constructed for the Cantal PDO farms by considering their environment and particularities. In consequence, the stakeholders have taken ownership of it. It could be used by other ruminant systems based on grassland. The development of numerous indicators enhanced the pool of existing indicators. This assessment method was sensitive and allowed for the discrimination between farms.     

Integrating Comparative Risk Assessment with Multi-Criteria Decision Analysis to Manage Contaminated Sediments: An Example for the New York/New Jersey Harbor

Current environmental challenges involve complex assessment and analysis of tradeoffs among differing criteria, expectations, and levels of certainty. Using multi-criteria decision analysis in combination with comparative risk assessment, a systematic and transparent framework can be created to integrate different types and sources of decision-relevant information. Although examples of decision analysis abound in the literature and academic efforts, its use within the practice of risk assessment and environmental management is still being established. This article provides an example of the use of decision analysis study that builds on a previous screening-level, comparative risk assessment of contaminated sediment from the New York/New Jersey harbor area. Using multi-criteria decision analysis, we explored the effect of different criteria weights, utility functions, and cost estimates on the ranking of seven contaminated sediment management alternatives. Values used in weighting decision criteria were surveyed from two interaction sessions with sediment management professionals.     

Nonlinearity,fuzziness and incommensurability in indicator-based assessments of vulnerability to climate change: A new mathematical framework

The earth’s climate system is highly nonlinear and the vulnerability of a community to a climate hazard is no exception. While this fact is widely accepted, indicator-based vulnerability assessments (IBVA) hardly ever take such nonlinearities into account. This is mainly due to the fact that the majority of assessment studies use methods based on Multiple Attribute Utility Theory (MAUT) (e.g. simple additive weight or multiplicative aggregation) to aggregate indicators. These methods convert all indicators into a global utility function and produce only a linear, threshold-free scaling of the effects of an indicator on vulnerability. In a previous paper, we showed that outranking procedures developed in decision-making science offer a more theoretically-sound approach to aggregation because they allow the analyst to incorporate the incommensurability, fuzziness and uncertainty associated with indicators. In this paper, we develop a new mathematical framework for vulnerability in order to clearly identify various sources of nonlinearity and incommensurability in vulnerability assessments. We then propose a new outranking formulation which can accommodate both and can be used to conduct assessments at different scales. We do so by introducing the concept of harm criterion as a mediator between an indicator and the vulnerability it represents. The new assessment approach can aggregate a mix of indicators with various degrees of subjectivity and non-linearity, without converting them into a single utility function and without requiring them to be mutually compensating.We illustrate the proposed approach by applying it to a simplified model of urban vulnerability to heat, focusing on the non-linear relationship between mortality and temperature above a ‘comfort temperature’, long evidenced in the epidemiological literature. We compare vulnerability rankings yielded by linear and non-linear characterizations of the relationship between temperature and mortality and find that the incorporation of non-linearity can have a significant impact on the rankings.     

Scientists' Assessment of the Impact of Housing and Management on Animal Welfare

A total of 22 scientists contributed to a consensus-oriented conceptual framework for assessment of farm animal welfare, addressing priority issues in cattle, pigs, and chickens. They used the Delphi method, in which participants contributed anonymously through e-mail. The framework puts welfare in an evolutionary context. Welfare problems arise when animals are kept in environments to which they have difficulty adapting. For integrated welfare assessment, design criteria and welfare performance criteria must be used in which the former may be weighted based on the latter, which, in turn, may be weighted based on the aspects of intensity, duration, and incidence. The main performance criteria were pathological states, abnormal behaviors, and behavioral restrictions. Space, social conditions, and substrates were the main design criteria. Conventional housing systems generally ranked lower than alternative systems. In this article, we show that a broad consensus basis exists among welfare scientists concerning the assessment of farm animal welfare.     

Scientists' Assessment of the Impact of Housing and Management on Animal Welfare

A total of 22 scientists contributed to a consensus-oriented conceptual framework for assessment of farm animal welfare, addressing priority issues in cattle, pigs, and chickens. They used the Delphi method, in which participants contributed anonymously through e-mail. The framework puts welfare in an evolutionary context. Welfare problems arise when animals are kept in environments to which they have difficulty adapting. For integrated welfare assessment, design criteria and welfare performance criteria must be used in which the former may be weighted based on the latter, which, in turn, may be weighted based on the aspects of intensity, duration, and incidence. The main performance criteria were pathological states, abnormal behaviors, and behavioral restrictions. Space, social conditions, and substrates were the main design criteria. Conventional housing systems generally ranked lower than alternative systems. In this article, we show that a broad consensus basis exists among welfare scientists concerning the assessment of farm animal welfare.     

Multi-criteria decision analysis to select metrics for design and monitoring of sustainable ecosystem restorations

The selection of metrics for ecosystem restoration programs is critical for improving the quality and utility of design and monitoring programs, informing adaptive management actions, and characterizing project success. The metrics selection process, that in practice is left to the subjective judgment of stakeholders, is often complex and should simultaneously take into account monitoring data, environmental models, socio-economic considerations, and stakeholder interests. With limited funding, it is often very difficult to balance the importance of multiple metrics, often competing, intended to measure different environmental, social, and economic aspects of the system. To help restoration planners and practitioners develop the most useful and informative design and monitoring programs, we propose the use of multi-criteria decision analysis (MCDA) methods, broadly defined, to select optimal ecosystem restoration metric sets. In this paper, we apply and compare two MCDA methods, multi-attribute utility theory (MAUT), and probabilistic multi-criteria acceptability analysis (ProMAA), for a hypothetical river restoration case study involving multiple stakeholders with competing interests. Overall, the MCDA results in a systematic, quantitative, and transparent evaluation and comparison of potential metrics that provides planners and practitioners with a clear basis for selecting the optimal set of metrics to evaluate restoration alternatives and to inform restoration design and monitoring. In our case study, the two MCDA methods provide comparable results in terms of selected metrics. However, because ProMAA can consider probability distributions for weights and utility values of metrics for each criterion, it is most likely the best option for projects with highly uncertain data and significant stakeholder involvement. Despite the increase in complexity in the metrics selection process, MCDA improves upon the current, commonly-used ad-hoc decision practice based on consultations with stakeholders by applying and presenting quantitative aggregation of data and judgment, thereby increasing the effectiveness of environmental design and monitoring and the transparency of decision making in restoration projects.