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Defining the breeding goal for a sheep breed including production and functional traits using market data

Published online by Cambridge University Press:  16 November 2017

A. Theodoridis*
Affiliation:
School of Veterinary Medicine, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
A. Ragkos
Affiliation:
Agricultural Economics Research Institute, ELGO Demeter, Terma Alkmanos Str. 11528, Athens, Greece
G. Rose
Affiliation:
School of Veterinary Medicine, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
D. Roustemis
Affiliation:
Center of Animal Genetic Improvement, 57011, Nea Mesimvria, Greece
G. Arsenos
Affiliation:
School of Veterinary Medicine, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
*
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Abstract

In this study, the economic values for production and functional traits of dairy sheep are estimated through the application of a profit function model using farm-level technical and economic data. The traits incorporated in the model were milk production, prolificacy, fertility, milking speed, longevity and mastitis occurrence. The economic values for these traits were derived as the approximate partial derivative of the specified profit function. A sensitivity analysis was also conducted in order to examine how potential changes in input and output prices would affect the breeding goal. The estimated economic values of the traits revealed their economic impact on the definition of the breeding goal for the specified production system. Milk production and fertility had the highest economic values (€40.30 and €20.28 per standard genetic deviation (SDa)), while, mastitis only had a low negative value of −0.57 €/SDa. Therefore, breeding for clinical mastitis will have a minor impact on farm profitability because it affects a small proportion of the flock and has low additive variance. The production traits, which include milk production, prolificacy and milking speed, contributed most to the breeding goal (70.0%), but functional traits still had a considerable share (30.0%). The results of this study highlight the importance of the knowledge of economic values of traits in the design of a breeding program. It is also suggested that the production and functional traits under consideration can be categorized as those which can be efficiently treated through genetic improvement (e.g. milk production and fertility) while others would be better dealt with through managerial interventions (e.g. mastitis occurrence). Also, sub-clinical mastitis that affects a higher proportion of flocks could have a higher contribution to breeding goals.

Type
Research Article
Copyright
© The Animal Consortium 2017 

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References

Agricultural Research Council 1980. The nutrient requirements of ruminant livestock. Technical review. Agriculture Research Council Working Party. Commonwealth Agricultural Bureaux, Farnham Royal, Slough, UK.Google Scholar
Barillet, F, Rupp, R, Mignon-Grasteau, S, Astruc, J-M and Jacquin, M 2001. Genetic analysis for mastitis resistance and milk somatic cell score in French Lacaune dairy sheep. Genetics Selection Evolution 33, 397415.Google Scholar
Bourdon, RM 1998. Shortcomings of current genetic evaluation systems. Journal of Animal Science 76, 23082323.Google Scholar
Brascamp, EW, Smith, C and Guy, DR 1985. Derivation of economic weights from profit equation. Animal Production 40, 175180.Google Scholar
Bytyqi, H, Fuerst-Waltl, B, Mehmeti, H and Baumung, R 2015. Economic values for production traits for different sheep breeds in Kosovo. Italian Journal of Animal Science 14, 603609.CrossRefGoogle Scholar
Cannas, A, Tedeschi, LO, Fox, DG, Pell, AN and Soest, PJV 2004. A mechanistic model for predicting the nutrient requirements and feed biological values for sheep. Journal of Animal Science 82, 149169.Google Scholar
Commonwealth Scientific and Industrial Research Organization 1990. Standing Committee on Agriculture. Ruminants Subcommittee. Feeding standards for Australian livestock. In Ruminants. CSIRO Publications, East Melbourne, Australia.Google Scholar
Fuerst-Waltl, B and Baumung, R 2009. Economic values for performance and functional traits in dairy sheep. Italian Journal of Animal Science 8, 341357.Google Scholar
Gade, S, Stamer, E, Junge, W and Kalm, E 2006. Estimates of genetic parameters for milkability fromautomatic milking. Livestock Science 104, 135146.Google Scholar
Gelasakis, AI, Angelidis, AS, Giannakou, R, Filioussis, G, Kalamaki, MS and Arsenos, G 2016. Bacterial subclinical mastitis and its effect on milk yield in low-input dairy goat herds. Journal of Dairy Science 99, 36983708.Google Scholar
Gelasakis, A, Valergakis, G, Arsenos, G and Banos, G 2012. Description and typology of intensive Chios dairy sheep farms in Greece. Journal of Dairy Science 95, 30703079.Google Scholar
Goddard, ME 1998. Consensus and debate in the definition of breeding objectives. Journal of Dairy Science 81, 618.Google Scholar
Groen, AbF, Steine, T, Colleau, JJ, Pedersen, J, Pribyl, J and Reinsch, N 1997. Economic values in dairy cattle breeding, with special reference to functional traits. Report of an EAAP-working group. Livestock Production Science 49, 121.Google Scholar
Haghdoost, A, Shadparvar, AA, Nasiri, MTB and Fayazi, J 2008. Estimates of economic values for traits of Arabic sheep in village system. Small Ruminant Research 80, 9194.Google Scholar
Hazel, LN 1943. The genetic basis for constructing selection indexes. Genetics 28, 476490.CrossRefGoogle ScholarPubMed
Jarrige, R 1989. Ruminant nutrition: recommended allowances and feed tables. INRA, John Libbey Eurotext, Paris.Google Scholar
Karanikolas, P and Martinos, N 2012. Greek agriculture facing crisis: problems and prospects. Retrieved on 3 April 2014 from http://ardin-rixi.gr/archives/3811 (in Greek).Google Scholar
Kalaisakis, P 1982. Applied animal nutrition. Stamoulis Publications, Athens. (in Greek).Google Scholar
Kominakis, A, Nitter, G, Fewson, D and Rogdakis, E 1997. Evaluation of the efficiency of alternative selection schemes and breeding objectives in dairy sheep of Greece. Animal Science 64, 453461.Google Scholar
Kosgey, IS, Van Arendonk, JA and Baker, RL 2004. Economic values for traits in breeding objectives for sheep in the tropics: impact of tangible and intangible benefits. Livestock Production Science 88, 143160.Google Scholar
Kosgey, IS, Van Arendonk, JAM and Baker, RL 2003. Economic values for traits of meat sheep in medium to high production potential areas of the tropics. Small Ruminant Research 50, 187202.Google Scholar
Krupová, Z, Oravcova, M, Krupa, E and Peskovicova, D 2008. Methods for calculating economic weights of important traits in sheep. Slovak Journal of Animal Science 41, 2429.Google Scholar
Krupová, Z, Wolfova, M, Wolf, J, Orancova, M, Margetin, M, Peskovicova, D, Krupa, E and Daňo, J 2009. Economic values for dairy sheep breeds in Slovakia. Asian – Australian Journal of Animal Science 22, 16931702.Google Scholar
Liamadis, D 1988. Energy requirements – nutritional needs of farm animals. University notes, Thessaloniki. (in Greek).Google Scholar
Legarra, A, Ramó, NM, Ugarte, E and Pérez-Guzmán, MD 2007a. Economic weights of fertility, prolificacy, milk yield and longevity in dairy sheep. Animal: An International Journal of Animal Bioscience 1, 193203.Google Scholar
Legarra, A, Ramó, M, Ugarte, E, Pérez-Guzmán, MD and Arranz, J 2007b. Economic weights of somatic cell score in dairy sheep. Animal 1, 205212.Google Scholar
Mavrogenis, AP, Koumas, A, Kakoyiannis, CK and Taliotis, CH 1995. Use of somatic cell counts for the detection of subclinical mastitis in sheep. Small Ruminant Research 17, 7984.Google Scholar
Nielsen, HM and Amer, PR 2007. An approach to derive economic weights in breeding objectives using partial profile choice experiments. Animal 1, 12541262.Google Scholar
O’Brien, AC, McHugh, N, Wall, E, Pabiou, T, McDermott, K, Randles, S, Fair, S and Berry, DP 2017. Genetic parameters for lameness, mastitis and dagginess in a multi-breed sheep population. Animal 11, 911919.Google Scholar
Olesen, I, Alfnes, F, Rora, MB and Kolstad, K 2010. Eliciting consumers’ willingness to pay for organic and welfare-labelled salmon in a non-hypothetical choice experiment. Livestock Science 127, 218226.Google Scholar
Panhellenic Confederation of Unions of Agricultural Cooperatives 2013. Recent Developments in the Agricultural Economy of Greece. PASEGES, Athens, Greece.Google Scholar
Ploumi, K, Belibasaki, S and Triantaphyllidis, G 1998. Some factors affecting daily milk yield and composition in a flock of Chios ewes. Small Ruminant Research 28, 8992.Google Scholar
Ponzoni, RW 1986. A profit equation for the definition of the breeding objective of Australian merino sheep. Journal of Animal Breeding and Genetics 103, 342357.Google Scholar
Pulina, G, Serra, A, Cannas, A and Rossi, G 1989. Determinazione e stima del valore energetico di latte di pecore di razza sarda (Measurement and prediction of energetic value of milk of Sarda ewes). Atti della Societa Italiana delle Scienze Veterinarie 43, 18671870.Google Scholar
Ragkos, A and Abas, Z 2015. Using the choice experiment method in the design of breeding goals in dairy sheep. Animal 9, 208217.CrossRefGoogle ScholarPubMed
Ragkos, A, Koutsou, S and Manousidis, T 2016. In search of strategies to face the economic crisis: evidence from Greek farms. South European Society and Politics 21 (3), 319337.Google Scholar
Ragkos, A, Siasiou, A, Galanopoulos, K and Lagka, V 2014. Mountainous grasslands sustaining traditional livestock systems: the economic performance of sheep and goat transhumance in Greece. Options Mediterraneennes 109, 575579.Google Scholar
Rauw, WM, Kanis, E, Noordhuizen-Stassen, EN and Grommers, FJ 1998. Undesirable side effects of selection for high production efficiency in farm animals: a review. Livestock Production Science 56, 1533.Google Scholar
Roustemis, D 2012. Design of the breeding goal for Chios sheep. Doctoral disseration. Democritus University of Thrace, Greece (in Greek).Google Scholar
Theodoridis, A, Ragkos, A, Roustemis, D, Arsenos, G, Abas, Z and Sinapis, E 2014. Technical indicators of economic performance in dairy sheep farming. Animal 8, 133140.CrossRefGoogle ScholarPubMed
Theodoridis, A, Ragkos, A, Roustemis, D, Galanopoulos, K, Abas, Z and Sinapis, E 2012. Assessing technical efficiency of Chios sheep farms with data envelopment analysis. Small Ruminant Research 107, 8591.Google Scholar
Tolone, M, Riggio, V, Maizon, DO and Portolano, B 2011. Economic values for production and functional traits in Valle del Belice dairy sheep using profit functions. Small Ruminant Research 97, 4147.Google Scholar
Tozer, PR and Stokes, JR 2002. Producer breeding objectives and optimal sire selection. Journal of Dairy Science 85, 35183525.Google Scholar
Valergakis, GE, Arsenos, G, Basdagianni, Z and Banos, G 2008. Grouping strategies and lead factors for ration formulation in milking ewes of the Chios breed. Livestock Science 115, 211218.Google Scholar
Van Middelaar, CE, Berentsen, PBM, Dijkstra, J, Van Arendonk, JAM and De Boer, IJM 2015. Effect of feed-related farm characteristics on relative values of genetic traits in dairy cows to reduce greenhouse gas emissions along the chain. Journal of Dairy Science 98, 48894903.Google Scholar
Wolfová, M, Štípková, M and Wolf, J 2005. Economic value of mastitis incidence in dairy herds in the Czech Republic. In 56th Annual Meeting of the EAAP. Upsala, Sweden.Google Scholar
Wolfová, M, Wolf, J, Kvapilík, J and Kica, J 2007. Selection for profit in cattle: I. Economic weights for purebred dairy cattle in the Czech Republic. Journal of Dairy Science 90, 24422455.Google Scholar
Wolfová, M, Wolf, J, Přibyl, J, Zahrádková, R and Kica, J 2005. Breeding objectives for beef cattle used in different production systems: 1. Model development. Livestock Production Science 95, 201215.Google Scholar
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