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Impact of including growth, carcass and feed efficiency traits in the breeding goal for combined milk and beef production systems

Published online by Cambridge University Press:  09 September 2016

P. Hietala*
Affiliation:
Department of Agricultural Sciences, University of Helsinki, P.O. Box 28, FI-00014 Helsinki, Finland
J. Juga
Affiliation:
Department of Agricultural Sciences, University of Helsinki, P.O. Box 28, FI-00014 Helsinki, Finland
*
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Abstract

Improving feed efficiency in dairy cattle could result in more profitable and environmentally sustainable dairy production through lowering feed costs and emissions from dairy farming. In addition, beef production based on dairy herds generates fewer greenhouse gas emissions per unit of meat output than beef production from suckler cow systems. Different scenarios were used to assess the profitability of adding traits, excluded from the current selection index for Finnish Ayrshire, to the breeding goal for combined dairy and beef production systems. The additional breeding goal traits were growth traits (average daily gain of animals in the fattening and rearing periods), carcass traits (fat covering, fleshiness and dressing percentage), mature live weight (LW) of cows and residual feed intake (RFI) traits. A breeding scheme was modeled for Finnish Ayrshire under the current market situation in Finland using the deterministic simulation software ZPLAN+. With the economic values derived for the current production system, the inclusion of growth and carcass traits, while preventing LW increase generated the highest improvement in the discounted profit of the breeding program (3.7%), followed by the scenario where all additional traits were included simultaneously (5.1%). The use of a selection index that included growth and carcass traits excluding LW, increased the profit (0.8%), but reduced the benefits resulted from breeding for beef traits together with LW. A moderate decrease in the profit of the breeding program was obtained when adding only LW to the breeding goal (−3.1%), whereas, adding only RFI traits to the breeding goal resulted in a minor increase in the profit (1.4%). Including beef traits with LW in the breeding goal showed to be the most potential option to improve the profitability of the combined dairy and beef production systems and would also enable a higher rate of self-sufficiency in beef. When considering feed efficiency related traits, the inclusion of LW traits in the breeding goal that includes growth and carcass traits could be more profitable than the inclusion of RFI, because the marginal costs of measuring LW can be expected to be lower than for RFI and it is readily available for selection. In addition, before RFI can be implemented as a breeding objective, the genetic correlations between RFI and other breeding goal traits estimated for the studied population as well as information on the most suitable indicator traits for RFI are needed to assess more carefully the consequences of selecting for RFI.

Type
Research Article
Copyright
© The Animal Consortium 2016 

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References

Åby, BA, Kantanen, J, Aass, L and Meuwissen, T 2014. Current status of livestock production in the Nordic countries and future challenges with a changing climate and human population growth. Acta Agriculturae Scandinavica Section A Animal Science 64, 7397.Google Scholar
Banos, G and Coffey, MP 2012. Technical note: prediction of liveweight from linear conformation traits in dairy cattle. Journal of Dairy Science 94, 21702175.Google Scholar
Berry, DP and Crowley, JJ 2013. Cell biology symposium: genetics of feed efficiency in dairy and beef cattle. Journal of Animal Science 91, 15941613.Google Scholar
Brøndum, RF, Rius-Vilarrasa, E, Strandén, I, Su, G, Guldbrandtsen, B, Fikse, WF and Lund, MS 2011. Reliabilities of genomic prediction using combined reference data of the Nordic Red dairy cattle populations. Journal of Dairy Science 94, 47004707.CrossRefGoogle ScholarPubMed
Connor, EE 2015. Invited review: Improving feed efficiency in dairy production: challenges and possibilities. Animal 9, 395408.CrossRefGoogle ScholarPubMed
Daetwyler, HD, Pong-Wong, R, Villanueva, B and Wooliams, JA 2010. The impact of genetic architecture on genome wide evaluation methods. Genetics 185, 10211031.CrossRefGoogle ScholarPubMed
Daetwyler, HD, Villanueva, B and Wooliams, JA 2008. Accuracy of predicting the genetic risk of diseases using genome-wide approach. PLoS One 3, e3395.Google Scholar
Egger-Danner, C, Cole, JB, Pryce, JE, Gengler, N, Heringstad, B, Bradley, A and Stock, KF 2015. Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits. Animal 9, 191207.Google Scholar
Flysjö, A, Cederberg, C, Henriksson, M and Ledgard, S 2012. The interaction between milk and beef production and emissions from land use change – critical considerations in life cycle assessment and carbon footprint studies of milk. Journal of Cleaner Production 28, 134142.CrossRefGoogle Scholar
Gao, H, Lund, MS, Zhang, Y and Su, G 2013. Accuracy of genomic prediction using different models and response variables in the Nordic Red cattle population. Journal of Animal Breeding and Genetics 130, 333340.CrossRefGoogle ScholarPubMed
Gerber, PJ, Steinfeld, H, Henderson, B, Mottet, A, Opio, C, Dijkman, J, Falcucci, A and Tempio, G 2013. Tackling climate change through livestock – a global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy.Google Scholar
Gonzalez-Recio, O, Pryce, JE, Haile-Mariam, M and Hayes, BJ 2014. Incorporating heifer feed efficiency in the Australian selection index using genomic selection. Journal of Dairy Science 97, 38833893.CrossRefGoogle ScholarPubMed
Groen, A and Vos, H 1995. Genetic parameters for body weight and growth in Dutch Black and White replacement stock. Livestock Production Science 41, 201206.Google Scholar
Harper, G and Henson, S 2001. Consumer concerns about animal welfare and the impact on food choice. EU FAIR CT98-3678, Centre for Food Economics Research, The University of Reading, Reading, UK.Google Scholar
Hietala, P, Bouquet, P and Juga, J 2014a. Effect of replacement rate, crossbreeding and sexed semen on the efficiency of beef production from dairy herds in Finland. Acta Agriculturae Scandinavica Section A Animal science 64, 199209.Google Scholar
Hietala, P, Wolfová, M, Wolf, J, Kantanen, J and Juga, J 2014b. Economic values of production and functional traits, including residual feed intake, in Finnish milk production. Journal of Dairy Science 94, 10921106.Google Scholar
Kargo, M, Hjortø, L, Toivonen, M, Eriksson, JA, Aamand, GB and Pedersen, J 2014. Economic basis for the Nordic Total Merit index. Journal of Dairy Science 97, 78797888.CrossRefGoogle ScholarPubMed
Karhula, T and Kässi, P 2010. Lihanautatilojen taloudellinen tilanne Suomessa ja vertailumaissa. In Kehitystä naudanlihantuotantoon I (Towards more efficient beef production I) (ed. A Huuskonen), pp. 934. Tampereen yliopistopaino Juvenes Print Ltd., Tampere, Finland. (In Finnish with English abstract).Google Scholar
Koch, RM, Swiger, LA, Chambers, D and Gregory, KE 1963. Efficiency of feed use in beef cattle. Journal of Animal Science 22, 486494.CrossRefGoogle Scholar
Kvalnes, T, Engen, S, Sæther, B-E and Jensen, H 2013. Package ‘lmf’: Functions for estimation and inference of selection in age-structured populations, R package version1.0. Retrieved on 11 April 2015 from http://CRAN.R-project.org/package=lmf.Google Scholar
McParland, S, Lewis, E, Kennedy, E, Moore, S, McCarthy, B, O’Donovan, M, Butler, S, Pryce, JE and Berry, DP 2014. Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows. Journal of Dairy Science 97, 58635871.Google Scholar
NAV 2013. NAV routine genetic evaluation of Dairy Cattle – data and genetic models, Nordic Cattle Genetic Evaluation (NAV). Retrieved on 10 December 2014 from http://www.nordicebv.info/NR/rdonlyres/5CD2E4DC-F82A-4809-A770-3022E270E205/0/PrinciplesNyeste.pdf.Google Scholar
NAV 2016. Genetic trends, Nordic Cattle Genetic Evaluation (NAV). Retrieved on 1 January 2016 from http://www.sweebv.info/ba52nycknav.aspx.Google Scholar
Nguyen, T, Hermansen, J and Mogensen, L 2010. Environmental consequences of different beef production systems in the EU. Journal of Cleaner Production 18, 756766.Google Scholar
Niemi, J and Ahlstedt, J 2013. Finnish agriculture and rural industries 2013. Publications 114a, MTT Agrifood Research Finland, Economic Research, Helsinki, Finland.Google Scholar
Oltenacu, PA and Broom, DM 2010. The impact of genetic selection for increased milk yield on the welfare of dairy cows. Animal welfare 19, 3949.Google Scholar
ProAgria 2013. Tuotosseurannan tulokset 2013 (Results of Finnish National milk-recording for the year 2013). Retrieved on 5 September 2014 from https://www.proagria.fi/sites/default/files/attachment/tuotosseurannan_tulokset_2013_nettiin.pdf.Google Scholar
Pryce, JE, Gonzalez-Recio, O, Nieuwhof, G, Wales, WJ, Coffey, MP, Hayes, BJ and Goddard, ME 2015. Hot topic: definition and implementation of a breeding value for feed efficiency in dairy cows. Journal of Dairy Science 98, 73407350.CrossRefGoogle ScholarPubMed
Pryce, JE, Wales, WJ, de Haas, Y, Veerkamp, RF and Hayes, BJ 2014. Genomic selection for feed efficiency in dairy cattle. Animal 8, 110.CrossRefGoogle ScholarPubMed
Statistic Finland 2016. Finland in figures: agriculture, forestry and fishery: self-sufficiency in foodstuff. Retrieved on 10 April 2016 from http://tilastokeskus.fi/tup/suoluk/suoluk_maatalous_en.html.Google Scholar
Thomasen, JR, Egger-Danner, C, Willam, A, Guldbrandtsen, B, Lund, MS and Sørensen, AC 2014. The optimal genomic selection strategy in a small dairy cattle breeding program still involves progeny testing. Journal of Dairy Science 97, 458470.Google Scholar
Täubert, H, Reinhardt, F and Simianer, H 2010. ZPLAN+ – a new software to evaluate and optimize animal breeding programs. In Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 1 to 6 August 2010, Leipzig, Germany.Google Scholar
Vallimont, JE, Dechow, JD, Daubert, JM, Dekleva, MW, Blum, JW, Liu, W, Varga, GA, Heinrichs, AJ and Baumrucker, JR 2013. Short communication: feed utilization and its associations with fertility and productive life in 11 commercial Pennsylvania tie-stall herds. Journal of Dairy Science 96, 12511254.CrossRefGoogle ScholarPubMed
Veerkamp, RF 1998. Selection for economic efficiency of dairy cattle using information on live weight and feed intake: a review. Journal of Dairy Science 81, 11091119.Google Scholar
Zehetmeier, M, Baudracco, J, Hoffmann, H and Heißenhuber, A 2012. Does increasing milk yield per cow reduce greenhouse gas emissions? A system approach. Animal 6, 154166.CrossRefGoogle ScholarPubMed
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