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

Published online by Cambridge University Press:  19 July 2016

P. Martín ,
I. Traulsen ,
C. Buxadé  and
J. Krieter
P. Martín*
Affiliation:
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Olshausenstr. 40, 24098 Kiel, Germany
I. Traulsen
Affiliation:
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Olshausenstr. 40, 24098 Kiel, Germany
C. Buxadé
Affiliation:
Department of Animal Production, ETSIA, Polytechnic University of Madrid, Ciudad Universitaria, s/n, 28040 Madrid, Spain
J. Krieter
Affiliation:
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Olshausenstr. 40, 24098 Kiel, Germany
*
†E-mail: pfernandez@tierzucht.uni-kiel.de
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Abstract

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.

Keywords

growing pigsmulti-criteria evaluationWelfare Quality®
Type
Research Article
Information
animal , Volume 11 , Issue 3 , March 2017 , pp. 466 - 477
Copyright
© The Animal Consortium 2016 

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References

Animal Welfare Indicators (AWIN) 2015. AWIN welfare assessment protocol for sheep. DOI:10.13130/AWIN_SHEEP_2015.Google Scholar
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.Google Scholar
Botreau, R, Bonde, M, Butterworth, A, Perny, P, Bracke, MBM, Capdeville, J and Veissier, I 2007a. Aggregation of measures to produce an overall assessment of animal welfare. Part 1: a review of existing methods. Animal 1, 1179–1187.Google Scholar
Botreau, R, Bracke, MBM, Perny, P, Butterworth, A, Capdeville, J, van Reenen, CG and Veissier, I 2007b. 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.CrossRefGoogle Scholar
Bouyssou, D, Marchant, T, Perny, P, Pirlot, M, Tsoukias, A and Vincke, P 2000. Evaluation and decision models: a critical perspective. Kluwer Academic, Dordrecht, the Netherlands.Google Scholar
Bouyssou, D, Marchant, T, Perny, P, Pirlot, M, Tsoukias, A and Vincke, P 2006. Evaluation and decision models with multiple criteria: stepping stones for the analyst. International Series in Operations Research and Management Science vol. 86, Springer, Boston, MA, USA.Google Scholar
Farm Animal Welfare Council 1992. FAWC updates the five freedoms. The Veterinary Record 17, 357.Google Scholar
Grabisch, M 1996. The application of fuzzy integrals in multicriteria decision making. European Journal of Operational Research 89, 445–456.CrossRefGoogle 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.CrossRefGoogle Scholar
Keeney, LR and Raiffa, H 1976. Decisions with multiple objectives: preferences and values tradeoffs. Wiley, New York, USA.Google Scholar
Leeb, C, Main, DCJ, Whay, HR and Webster, AJF 2004. Bristol welfare assurance programme, cattle assessment. University of Bristol, Bristol, UK.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 systems. Theory and methods – book series: communication in computer and information science vol.80, ed. E Hüllermeier, R Kruse and F Hoffmann), pp. 148–157. Springer – Verlag, Berlin 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
Merad, M, Dechy, N, Serir, L, Grabisch, M and Marcel, F 2013. Using a multi-criteria decision aid methodology to implement sustainable development principles within an organization. European Journal of Operational Research 224, 603–613.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.Google Scholar
Parnell, GS, Brensik, TA, Tani, SN and Johnson, ER 2013. Handbook of decision analysis. John Wiley and sons, New York, USA.Google Scholar
Ramsay, JO 1988. Monotone regression splines in action. Statistical Science 3, 425–442.Google Scholar
Roy, B 1971. Problems and methods with multiple objective functions. Mathematical Programming 1, 239–266.Google Scholar
Saaty, TL 1980. The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill, New York, USA.Google Scholar
Temple, D, Dalmau, A, Ruiz de la Torre, JL, Manteca, X and Velarde, A 2011. 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.CrossRefGoogle Scholar
Vapnek, J and Chapman, M 2010. Legislative and regulatory options for animal welfare. FAO Legislative study 104. FAO, Rome, Italy.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® 2009. Welfare Quality® assessment protocol for fattening pigs. Welfare Quality® Consortium, Lelystad, the Netherlands.Google Scholar
Yager, R 1988. On ordered weighted averaging operators in multicriteria decision making. IEEE Transactions on Systems, Man and Cybernetics 18, 183–190.CrossRefGoogle Scholar
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