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Validation of a multi-criteria evaluation model for animal welfare

Published online by Cambridge University Press:  30 August 2016

P. Martín ,
I. Czycholl ,
C. Buxadé  and
J. Krieter
P. Martín*
Affiliation:
Institut für Tierzucht und Tierhaltung, Christian-Albrechts-Universität, Olshausenstr. 40, 24098 Kiel, Germany
I. Czycholl
Affiliation:
Institut für Tierzucht und Tierhaltung, Christian-Albrechts-Universität, Olshausenstr. 40, 24098 Kiel, Germany
C. Buxadé
Affiliation:
Departmento de Producción Animal, ETSIA, Universidad Politécnica de Madrid, Ciudad Universitaria, s/n, 28040 Madrid, Spain
J. Krieter
Affiliation:
Institut für Tierzucht und Tierhaltung, Christian-Albrechts-Universität, Olshausenstr. 40, 24098 Kiel, Germany
*
†E-mail: pfernandez@tierzucht.uni-kiel.de
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Abstract

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.

Keywords

growing pigsmulti-criteria evaluationWelfare Quality®
Type
Research Article
Information
animal , Volume 11 , Issue 4 , April 2017 , pp. 650 - 660
Copyright
© The Animal Consortium 2016 

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References

Bana e Costa, CA, de Corte, JM and Vansnick, JC 1999. The MACBETH approach: basic ideas, software, and an application. In Advances in decision analysis (ed. N Meskens and M Roubens), pp. 131–157. Kluwer Academic Publishers, Dordrecht, The Netherlands.Google Scholar
Bana e Costa, CA, de Corte, JM and Vansnick, JC 2004. On the mathematical foundations of MACBETH. In MCDA, multiple criteria decision analysis (ed. J Figueira, S Greco and M Ehrgott), pp. 409–442. Kluwer Academic Publishers, Dordrecht, The Netherlands.Google Scholar
Bana e Costa, CA, Lourenço, JC, Oliveira, MD and Bana e Costa, JC 2014. A socio-technical approach for group decision support in public strategic planning: the Pernambuco PPA case. Group Decision and Negotiation 23, 5–29.CrossRefGoogle Scholar
Botreau, R, Bracke, MBM, Perny, P, Butterworth, A, Capdeville, J, van Reenen, CG and Veissier, I 2007. Aggregation of measures to produce an overall assessment of animal welfare. Part 2: analysis of constraints. Animal 1, 1188–1197.Google Scholar
Botreau, R, Capdeville, J, Perny, P and Veissier, I 2008. Multicriteria evaluation of animal welfare at farm level: an application of MCDA methodologies. Foundations of Computing and Decision Science 33, 1–18.Google Scholar
Botreau, R, Veissier, I and Perny, P 2009. Overall assessment of animal welfare: strategy adopted in Welfare Quality. Animal Welfare 18, 363–370.Google Scholar
Choquet, G 1953. Theory of capacities. Annales de l’Institut Fourier 5, 131–295.Google Scholar
de Vries, M, Bokkers, EAM, van Schaik, G, Botreau, R, Engel, B, Dijkstra, T and de Boer, M 2013. Evaluating results of the Welfare Quality multi-criteria evaluation model for classification of dairy cattle welfare at herd level. Journal of Dairy Science 96, 1–10.Google Scholar
Farm Animal Welfare Council 1992. FAWC updates the five freedoms. The Veterinary Record 17, 357.Google Scholar
Geissman, JR and Schultz, RD 1991. Verification and validation of expert system. In Validating and verifying knowledge-based systems (ed. UG Gupta), pp. 12–19. IEEE Computer Society Press, Washington, DC, USA.Google Scholar
Grabisch, M 1996. The application of fuzzy integrals in multicriteria decision making. European Journal of Operational Research 89, 445–456.Google Scholar
Grabisch, M, Kojadinovic, I and Meyer, M 2008. A review of capacity identification methods for Choquet integral based multi-attribute utility theory, applications of the Kappalab R package. European Journal of Operational Research 186, 766–785.Google Scholar
Harrison, SR 1991. Validation of agricultural expert systems. Agricultural Systems 35, 265–285.Google Scholar
O’Keefe, RM, Osman, B and Smith, EP 1991. Validating expert system performance. In Validating and verifying knowledge-based systems (ed. UG Gupta), pp. 2–11. IEEE Computer Society Press, Washington, DC, USA.Google Scholar
Keeney, LR and Raiffa, H 1976. Decisions with multiple objectives: preferences and values tradeoffs. Wiley, New York, New York, USA.Google Scholar
Martín, P, Traulsen, I, Buxadé, C and Krieter, J 2016. Development of a multi-criteria evaluation system to assess growing pig welfare. Animal, doi:10.1017/S1751731116001464.Google Scholar
Mayag, B, Grabisch, M and Labreuche, C 2010. An interactive algorithm to deal with inconsistencies in the representation of cardinal information. In Information processing and management of uncertainty in knowledge-based system (ed. E Hüllermeier, R Kruse and F Hoffmann), pp. 148–157. Springer-Verlag, Berlin and Heidelberg, Germany.Google Scholar
Mayag, B, Grabisch, M and Labreuche, C 2011. A characterization of the 2-additive Choquet integral through cardinal information. Fuzzy Sets and Systems 184, 84–105.Google Scholar
Murofushi, T and Sugeno, M 1989. An interpretation of fuzzy measure and the Choquet integral as an integral with respect to a fuzzy measure. Fuzzy Sets and Systems 29, 201–227.CrossRefGoogle Scholar
Parnell, GS, Brensik, TA, Tani, SN and Johnson, ER 2013. Handbook of decision analysis. John Wiley and Sons, New York, New York, USA.Google Scholar
Qureshi, ME, Harrison, SR and Wegener, MK 1999. Validation of multicriteria analysis models. Agricultural Systems 62, 105–116.Google Scholar
Ramsay, JO 1988. Monotone regression splines in action. Statistical Science 3, 425–442.Google Scholar
Temple, D, Courboulay, C, Velarde, A, Dalmau, A and Manteca, X 2012a. The welfare of growing pigs in five different production systems in France and Spain: assessment of health. Animal Welfare 21, 257–271.Google Scholar
Temple, D, Courboulay, V, Manteca, X, Velarde, A and Dalmau, A 2012b. The welfare of growing pigs in five different production systems: assessment of feeding and housing. Animal 6, 656–667.Google Scholar
Temple, D, Dalmau, A, Ruiz de la Torre, JL, Manteca, X and Velarde, A 2011a. Application of the welfare quality protocol to assess growing pigs kept under intensive conditions in Spain. Journal of Veterinary Behavior: Clinical Applications and Research 6, 138–149.Google Scholar
Temple, D, Manteca, X, Dalmau, A and Velarde, A 2013. Assessment of test-retest reliability of animal-based measures on growing pig farms. Livestock Science 151, 35–45.Google Scholar
Temple, D, Manteca, X, Velarde, A and Dalmau, A 2011b. Assessment of animal welfare through behavioural parameters in Iberian pigs in intensive and extensive conditions. Applied Animal Behaviour Science 131, 29–39.Google Scholar
Tierschutzbund, D 2013. Kriterienkatalog für eine tiergerechte haltung und behandlung von mastschweinen im rahmen des tierschutzlabels ‘Für mehr tierschutz’. Deutscher Tierschutzbund e.V., Bonn, Germany.Google Scholar
Veissier, I, Jensen, KK, Botreau, R and Sandoe, P 2011. Highlighting ethical decisions underlying the scoring of animal welfare in the Welfare Quality scheme. Animal Welfare 20, 89–101.Google Scholar
Welfare Quality (WQ) 2009. Welfare Quality® assessment protocol for pigs. Welfare Quality® Consortium, Lelystad, The Netherlands.Google Scholar