Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-23T04:49:24.145Z Has data issue: false hasContentIssue false
  • English
  • Français

A preference-based approach to deriving breeding objectives: applied to sheep breeding

Published online by Cambridge University Press:  11 November 2011

T. J. Byrne ,
P. R. Amer ,
P. F. Fennessy ,
P. Hansen  and
B. W. Wickham
T. J. Byrne*
Affiliation:
AbacusBio Limited, PO Box 5585, Dunedin, New Zealand Department of Zoology, University of Otago, PO Box 56, Dunedin, New Zealand
P. R. Amer
Affiliation:
AbacusBio Limited, PO Box 5585, Dunedin, New Zealand
P. F. Fennessy
Affiliation:
AbacusBio Limited, PO Box 5585, Dunedin, New Zealand
P. Hansen
Affiliation:
Department of Economics, University of Otago, PO Box 56, Dunedin, New Zealand
B. W. Wickham
Affiliation:
Irish Cattle Breeding Federation Society Limited, Highfield House, Shinagh, Bandon, Co. Cork, Ireland
Get access

Abstract

Using internet-based software known as 1000Minds, choice-experiment surveys were administered to experts and farmers from the Irish sheep industry to capture their preferences with respect to the relative importance – represented by part-worth utilities – of target traits in the definition of a breeding objective for sheep in Ireland. Sheep production in Ireland can be broadly separated into lowland and hill farming systems; therefore, each expert was asked to answer the survey first as if he or she were a lowland farmer and second as a hill farmer. In addition to the experts, a group of lowland and a group of hill farmers were surveyed to assess whether, and to what extent, the groups’ preferences differ from the experts’ preferences. The part-worth utilities obtained from the surveys were converted into relative economic value terms per unit change in each trait. These measures – referred to as ‘preference economic values’ (pEVs) – were compared with economic values for the traits obtained from bio-economic models. The traits ‘value per lamb at the meat processor’ and ‘lamb survival to slaughter’ were revealed as being the two most important traits for the surveyed experts responding as lowland and hill farmers, respectively. In contrast, ‘number of foot baths per year for ewes’ and ‘number of anthelmintic treatments per year for ewes’ were the two least important traits. With the exception of ‘carcase fat class’ (P < 0.05), there were no statistically significant differences in the mean pEVs obtained from the surveyed experts under both the lowland and hill farming scenarios. Compared with the economic values obtained from bio-economic models, the pEVs for ‘lambing difficulty’ when the experts responded as lowland farmers were higher (P < 0.001); and they were lower (P < 0.001) for ‘carcase conformation class’, ‘carcase fat class’ (less negative) and ‘ewe mature weight’ (less negative) under both scenarios. Compared with surveyed experts, pEVs from lowland farmers differed significantly for ‘lambing difficulty’, ‘lamb survival to slaughter’, ‘average days to slaughter of lambs’, ‘number of foot baths per year for ewes’, ‘number of anthelmintic treatments per year for ewes’ and ‘ewe mature weight’. Compared with surveyed experts, pEVs from hill farmers differed significantly for ‘lambing difficulty’, ‘average days to slaughter of lambs’ and ‘number of foot baths per year for ewes’. This study indicates that preference-based tools have the potential to contribute to the definition of breeding objectives where production and price data are not available.

Keywords

choice experimenteconomic weightsbreeding objectivessheep
Type
Full Paper
Information
animal , Volume 6 , Special Issue 5: Symposium “Sustainable animal production in the tropics” (SAPT), Guadeloupe, France, November 15–18, 2010 , May 2012 , pp. 778 - 788
Copyright
Copyright © The Animal Consortium 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Amer, PR, Fox, GC 1992. Estimation of economic weights in genetic improvement using neoclassical production theory: an alternative to rescaling. Animal Production 54, 341–350.Google Scholar
Anonymous 2008. Sheep and sheepmeat: Lamb prices. Meat and livestock review and outlook 2008/09 (ed. Anonymous), 35pp. Bord Bia, Dublin. Retrieved September 4, 2010, from http://www.bordbia.ie/industryinfo/meat/pages/default.aspxGoogle Scholar
Belton, V, Stewart, TJ 2002. Multiple criteria decision analysis – an integrated approach. Kluwer Academic Publishers, Boston, MA, USA.CrossRefGoogle Scholar
Byrne, TJ, Amer, PR, Fennessy, PF, Cromie, AR, Keady, TWJ, Hanrahan, JP, McHugh, MP, Wickham, BW 2010. Breeding objectives for sheep in Ireland: a bio-economic approach. Livestock Science 132, 135–144.CrossRefGoogle Scholar
Byrne, TJ, Amer, PR, Fennessy, PF, Rohloff, RM, Cromie, A, Donnellan, P, Potterton, G, Hanrahan, JP, Wickham, BW 2009. Progress in the development of breeding schemes for the Irish sheep industry: the maternal lamb producer groups. Proceedings of the Association for the Advancement of Animal Breeding and Genetics, Barossa, South Australia, pp. 434–437.Google Scholar
Carson, RT, Louviere, JJ, Anderson, DA, Arabie, P, Bunch, DS, Hensher, DA, Johnson, RM, Kuhfeld, WF, Steinberg, D, Swait, J, Timmermans, H, Wiley, JB 1994. Experimental analysis of choice. Marketing Letters 5, 351–367.Google Scholar
Caussade, S, Ortúzar, JdD, Rizzi, LI, Hensher, DA 2005. Assessing the influence of design dimensions on stated choice experiment estimates. Transportation Research Part B: Methodological 39, 621–640.Google Scholar
Chrzan, K, Elrod, T 1995. Choice-based approach for large numbers of attributes. Marketing News 29, 37–41.Google Scholar
Dekkers, JCM, Gibson, JP 1998. Applying breeding objectives to dairy cattle improvement. Journal of Dairy Science 81, 19–35.CrossRefGoogle ScholarPubMed
Edel, C, Dempfle, L 2006. The use of the contingent valuation method to define breeding objectives: experiences from two studies on horse breeds. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil, CD-ROM communcation 31-08, 4pp.Google Scholar
Green, PE, Srinivasan, V 1990. Conjoint analysis in marketing: new developments with implications for research and practice. Journal of Marketing 54, 3–19.CrossRefGoogle Scholar
Hanrahan, JP 2008. Observations on the incidence of lambing assistance. Research Report 2008: Beef and Sheep Production Research & Animal Bioscience Research, Teagasc, Carlow, 4pp.Google Scholar
Hansen, P, Ombler, F 2009. A new method for scoring additive multi-attribute value models using pairwise rankings of alternatives. Journal of Multi-Criteria Decision Analysis 15, 87–107.CrossRefGoogle Scholar
Harris, DL 1970. Breeding for efficiency in livestock production: defining the economic objectives. Journal of Animal Science 30, 860–865.CrossRefGoogle Scholar
Hazel, LN 1943. The genetic basis for constructing selection indexes. Genetics 28, 476–490.CrossRefGoogle ScholarPubMed
Kempster, AJ 1981. Fat partition and distribution in the carcasses of cattle, sheep and pigs: a review. Meat Science 5, 83–98.Google Scholar
Lancaster, KJ 1966. A new approach to consumer theory. Journal of Political Economy 74, 132–157.Google Scholar
McClintock, AE, Cunningham, EP 1974. Selection in dual purpose cattle populations: defining the breeding objective. Animal Production 18, 237–247.Google Scholar
Murphy, C 2010. Surprise lift in sheep prices. Irish Independent Independent News and Media PLC, Dublin, Ireland. Retrieved January 26, 2011, from http://www.independent.ie/farming/news-features/surprise-lift-in-sheep-prices-2264864.html.Google Scholar
Nielsen, HM, Amer, PR 2007. An approach to derive economic weights in breeding objectives using partial profile choice experiments. Animal 1, 1254–1262.CrossRefGoogle ScholarPubMed
Nielsen, H, Olesen, I, Navrud, S, Kolstad, K, Amer, P 2011. How to consider the value of farm animals in breeding goals: a review of current status and future challenges. Journal of Agricultural and Environmental Ethics 24, 309–330.CrossRefGoogle Scholar
Olesen, I, Navrud, S, Kolstad, K 2006. Economic values of animal welfare in breeding goals. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil. CD-ROM communication 31-07.Google Scholar
Orme, BK 2010. Interpreting the results of conjoint analysis. In Getting started with conjoint analysis: strategies for product design and pricing research (ed. BK Orme), pp. 77–88. Research Publishers LLC, Madison, WI, USA.Google Scholar
Orme, BK, Alpert, MI, Christensen, E 1997. Assessing the validity of conjoint analysis – continued. In Sawtooth Software Research Paper Series, p. 20. Sawtooth Software Inc., Sequim. Retrieved February 23, 2011, from http://www.sawtoothsoftware.com/education/techpap.shtml.Google Scholar
Ponzoni, RW 1988. Accounting for both income and expense in the development of breeding objectives. Proceedings of the Australian Association of Animal Breeding and Genetics 7, 55–66.Google Scholar
Scarpa, R, Ruto, ESK, Kristjanson, P, Radeny, M, Drucker, AG, Rege, JEO 2003. Valuing indigenous cattle breeds in Kenya: an empirical comparison of stated and revealed preference value estimates. Ecological Economics 45, 409–426.CrossRefGoogle Scholar
Smith, C 1983. Effects of changes in economic weights on the efficiency of index selection. Journal of Animal Science 56, 1057–1064.CrossRefGoogle Scholar
Smith, KF, Fennessy, PF 2011. The use of conjoint analysis to determine the relative importance of specific traits as selection criteria for the improvement of perennial pasture species in Australia. Crop and Pasture Science 62, 355–365.CrossRefGoogle Scholar
Sölkner, J, Grausgruber, H, Okeyo, A, Ruckenbauer, P, Wurzinger, M 2008. Breeding objectives and the relative importance of traits in plant and animal breeding: a comparative review. Euphytica 161, 273–282.Google Scholar
Stevens, SS 1946. On the theory of scales of measurement. Science 103, 677–680.CrossRefGoogle ScholarPubMed
Sy, HA, Faminow, MD, Johnson, GV, Crow, G 1997. Estimating the values of cattle characteristics using an Ordered Probit Model. American Journal of Agricultural Economics 79, 463–476.CrossRefGoogle Scholar
Tano, K, Kamuanga, M, Faminow, MD, Swallow, B 2003. Using conjoint analysis to estimate farmer's preferences for cattle traits in West Africa. Ecological Economics 45, 393–407.Google Scholar
Toubia, O, Simester, DI, Hauser, JR, Dahan, E 2003. Fast polyhedral adaptive conjoint estimation. Marketing Science 22, 273–303.CrossRefGoogle Scholar
Vandepitte, WM, Hazel, LN 1977. The effect of errors in the economic weights on the accuracy of selection indexes. Annals Genetique Selection Animalia 9, 87–103.Google ScholarPubMed
von Rohr, P, Hofer, A, Kunzi, N 1999. Economic values for meat quality traits in pigs. Journal of Animal Science 77, 2633–2640.Google Scholar
Wurzinger, M, Ndumu, D, Baumung, R, Drucker, AG, Okeyo, AM, Semambo, DK, Sölkner, J 2006. Assessing stated preferences through the use of choice experiments: valuing (re)production v. aesthetics in the breeding goals of Ugandan Ankole cattle breeders. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil. CD-ROM communication 31-09.Google Scholar