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Implementation in breeding programmes

Published online by Cambridge University Press:  30 July 2013

M. P. Coffey*
Animal Veterinary Sciences Group, Scotlands Rural College, EH25 9RG Edinburgh, UK
S. McParland
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
C. Bastin
University of Liège, Gembloux Agro-Bio Tech, Animal Science Unit, Gembloux, Belgium
E. Wall
Animal Veterinary Sciences Group, Scotlands Rural College, EH25 9RG Edinburgh, UK
D. Berry
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
R. F. Veerkamp
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 8200 AB Lelystad, The Netherlands
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Genetic improvement is easy when selecting for one heritable and well-recorded trait at a time. Many industrialised national dairy herds have overall breeding indices that incorporate a range of traits balanced by their known or estimated economic value. Future breeding goals will contain more non-production traits and, in the context of this paper, traits associated with human health and cow robustness. The definition of Robustness and the traits used to predict it are currently fluid; however, the use of mid-infrared reflectance spectroscopic analysis of milk will help to create new phenotypes on a large scale that can be used to improve the human health characteristics of milk and the robustness of cows producing it. This paper describes the state-of-the-art in breeding strategies that include animal robustness (mainly energy status) and milk quality (as described by milk fatty acid profile), with particular emphasis on the research results generated by the FP7-funded RobustMilk project

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Copyright © The Animal Consortium 2013 

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Amer, PR 2011. Turning science on robust cattle into improved genetic selection decisions. Animal 1, 16.Google Scholar
Banos, G, Coffey, MP 2010. Genetic association between body energy measured throughout lactation and fertility in dairy cattle. Animal 4, 189199.Google Scholar
Banos, G, Coffey, MP, Veerkamp, RF, Berry, DP, Wall, E 2012. Merging and characterising phenotypic data on conventional and rare traits from dairy cattle experimental resources in three countries. Animal 6, 10401048.CrossRefGoogle ScholarPubMed
Barber, MC, Clegg, RA, Travers, MT, Vernon, RG 1997. Lipid metabolism in the lactating mammary gland. Biochimica et Biophysica Acta 1347, 101126.CrossRefGoogle ScholarPubMed
Bastin, C, Gengler, N, Soyeurt, H 2011. Phenotypic and genetic variability of production traits and milk FA contents across days in milk for Walloon Holstein first-parity cows. Journal of Dairy Science 94, 41524163.Google Scholar
Bastin, C, Berry, DP, Soyeurt, H, Gengler, N 2012a. Genetic correlations of days open with production traits and contents in milk of major fatty acids predicted by mid-infrared spectrometry. Journal of Dairy Science 95, 61136121.Google Scholar
Bastin, C, Soyeurt, H, Gengler, N 2012b. Genetic parameters of milk production traits and fatty acid contents in milk for Holstein cows in parity 1–3. Journal of Animal Breeding and Genetics. doi: 10.1111/jbg.12010.Google Scholar
Beam, SW, Butler, WR 1999. Effects of energy balance on follicular development and first ovulation in postpartum dairy cows. Journal of Reproduction and Fertility Supplement 54, 411424.Google Scholar
Berry, DP, Veerkamp, RF, Dillon, PG 2006. Phenotypic profiles for body weight, body condition score, energy intake, and energy balance across different parities and concentrate feeding levels. Livestock Science 104, 112.Google Scholar
Berry, DP, Bermingham, M, Good, M, More, SJ 2011. Genetics of animal health and disease in cattle. Irish Veterinary Journal 64, 5.Google Scholar
Bobe, G, Minick Bormann, JA, Lindberg, GL, Freeman, AE, Beitz, DC 2008. Short communication: estimates of genetic variation of milk fatty acids in US Holstein cows. Journal of Dairy Science 91, 12091213.Google Scholar
Calus, MPL, Groen, AF, De Jong, G 2002. Genotype × environment interaction for protein yield in Dutch dairy cattle as quantified by different models. Journal of Dairy Science 85, 31153123.Google Scholar
Coffey, MP, Simm, G, Brotherstone, S 2002. Energy balance for the first three lactations of dairy cows estimated using random regression. Journal of Dairy Science 85, 26692678.Google Scholar
Coffey, MP, Mottram, TB, McFarlane, N 2003. A feasibility study on the automatic recording of condition score in dairy cows. Proceedings of the 2003 British Society of Animal Science Winter Meeting. March, York, 131pp.CrossRefGoogle Scholar
Collard, BL, Dekkers, JCM, Petitclerc, D, Schaeffer, LR 2000. Relationships between energy balance and health traits of dairy cattle in early lactation. Journal of Dairy Science 11, 26832690.Google Scholar
de Haas, Y, Calus, MPL, Veerkamp, RF, Wall, E, Coffey, MP, Daetwyler, HD, Hayes, BJ, Pryce, JE 2012. Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian datasets. Journal of Dairy Science 95, 61036112.CrossRefGoogle Scholar
Dijkstra, J, van Zijderveld, SM, Apajalahti, JA, Bannink, A, Gerrits, WJJ, Newbold, JR, Perdok, HB, Bernends, H 2011. Relationships between methane production and milk fatty acid profile in dairy cattle. Animal of Feed Science and Technology 166–167, 590595.Google Scholar
Ebringer, L, Ferenčík, M, Krajčovič, J 2008. Beneficial health effects of milk and fermented dairy products – review. Folia Microbiol 53, 378394.Google Scholar
Friggens, NC, Newbold, JR 2007. Towards a biological basis for predicting nutrient partitioning: the dairy cow as an example. Animal 1, 8797.Google Scholar
Gengler, N, Troch, T, Vanderick, S, Bastin, C, Soyeurt, H 2012. Implementing a national routine genetic evaluation for milk fat compositions as first step towards genomic predictions. Proceedings of 2012 Interbull Meeting, Cork, Ireland.Google Scholar
Grummer, RR 1991. Effect on feed on the composition of milk fat. Journal of Dairy Science 74, 32283243.Google Scholar
Hammami, H, Rekik, B, Soyeurt, H, Bastin, C, Bay, E, Stoll, J, Gengler, N 2009. Accessing genotype by environment interaction using within- and across-country test-day random regression sire models. Journal of Animal Breeding and Genetics 126, 366377.Google Scholar
Haug, A, Høstmark, AT, Harstad, OM 2007. Bovine milk in human nutrition – a review. Lipids in Health and Disease 6, 25.Google Scholar
Horan, B, Mee, JF, O'Connor, P, Rath, M, Dillon, P 2004. The effect of strain of Holstein-Friesian cow and feed system on reproductive performance in seasonal-calving milk production systems. Animal Science 79, 453468.Google Scholar
Ipema, AH, Goense, D, Hogewerf, PH, Houwers, HWJ, van Roest, H 2008. Pilot study to monitor body temperature of dairy cows with a rumen bolus. Computers and Electronics in Agriculture 64, 4952.Google Scholar
Kempthorne, O, Nordskog, AW 1959. Restricted selection indices. Biometrics 15, 1019.Google Scholar
McCarthy, S, Berry, DP, Dillon, P, Rath, M, Horan, B 2007. Effect of Strain of Holstein-Friesian and feed system on calving performance, blood parameters and overall survival. Livestock Science 111, 218229.Google Scholar
McParland, S, Banos, G, Wall, E, Coffey, MP, Soyeurt, H, Veerkamp, RF, Berry, DP 2011. The use of mid-infrared spectrometry to predict body energy status of Holstein cows. Journal of Dairy Science 94, 36513661.Google Scholar
McParland, S, Banos, G, McCarthy, B, Lewis, E, Coffey, MP, O'Neill, B, O'Donovan, M, Wall, E, Berry, DP 2012. Validation of mid-infrared spectrometry in milk for predicting body energy status in Holstein-Friesian cows. Journal of Dairy Science 95, 72257235.CrossRefGoogle ScholarPubMed
Mele, M, Dal Zotto, R, Cassandro, M, Conte, G, Serra, A, Buccioni, A, Bittante, G, Secchiari, P 2009. Genetic parameters of conjugated linoleic acid, selected milk fatty acids, and milk fatty acid unsaturation of Italian Holstein-Frisian cows. Journal of Dairy Science 92, 392400.Google Scholar
Miglior, F, Muir, BL, Van Doormaal, BJ 2005. Selection indices in Holstein cattle of various countries. Journal of Dairy Science 88, 12551263.CrossRefGoogle ScholarPubMed
Nielsen, HM, 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, 309330.Google Scholar
Palmquist, DL, Baulieu, AD, Barbano, DM 1993. Feed and animal factors influencing milk fat composition. Journal of Dairy Science 76, 17531771.Google Scholar
Polat, B, Colak, A, Cengiz, M, Yanmaz, LE, Oral, H, Bastan, A, Kaya, S, Hayirli, A 2010. Sensitivity and specificity of infrared thermography in detection of subclinical mastitis in dairy cows. Journal of Dairy Science 93, 35253532.Google Scholar
Pryce, JE, Nielson, BL, Veerkamp, RF, Simm, G 1999. Genotype and feeding system effects and interactions for health and fertility traits in dairy cattle. Livestock Production Science 57, 193201.Google Scholar
Soyeurt, H, Bruwier, D, Romnee, J-M, Gengler, N, Bertozzi, C, Veselko, D, Dardenne, P 2009. Potential estimation of mineral contents in cow milk using mid-infrared spectrometry. Journal of Dairy Science 92, 24442454.Google Scholar
Soyeurt, H, Dehareng, F, Gengler, N, McParland, S, Wall, E, Berry, DP, Coffey, M, Dardenne, P 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. Journal of Dairy Science 84, 16571667.Google Scholar
Soyeurt, H, Bastin, C, Colinet, FG, Arnould, VM-R, Berry, DP, Wall, E, Dehareng, F, Nguyen, HN, Dardenne, P, Schefers, J, Vandenplas, J, Weigel, K, Coffey, M, Théron, L, Detilleux, J, Reding, E, Gengler, N, McParland, S 2012. Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis. Animal 6, 18301838.Google Scholar
Stoop, WM, Bovenhuis, H, Heck, JLM, Arendonk, JAM 2009. Effect of lactation stage and energy status on milk fat composition of Holstein-Friesian cows. Journal of Dairy Science 92, 14691478.Google Scholar
ten Napel, J, Calus, MPL, Mulder, HA, Veerkamp, RF 2009. Genetic concepts to improve robustness of dairy cows. In Breeding for robustness in cattle (ed. RR Marija Klopcic, J Philipsson and A Kuipers), pp. 288. EAAP Scientific Series—ISSN 0071-2477, Wageningen Academic Publishers, The Netherlands.Google Scholar
Van Haelst, YNT, Beeckman, A, Van Knegsel, ATM, Fievez, V 2008. Short communication: elevated concentrations of oleic acid and long-chain fatty acids in milk fat of multiparous subclinical ketotic cows. Journal of Dairy Science 91, 46834686.Google Scholar
Veerkamp, RF, Beerda, B 2007. Genetics and genomics to improve fertility in high producing dairy cows. Theriogenology 68S, S266S273.Google Scholar
Veerkamp, RF, Koenen, EPC, de Jong, G 2001. Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models. Journal of Dairy Science 84, 23272335.Google Scholar
Veerkamp, RF, Oldenbroek, JJ, Van Der Gaast, HJ, Van Der Werf, JHJ 2000. Genetic correlation between days until start of luteal activity and milk yield, energy balance and live weights. Journal of Dairy Science 3, 577583.Google Scholar
Wall, E, Brotherstone, S, Coffey, MP 2003. Genetic evaluation of fertility using direct and correlated traits. Journal of Dairy Science 86, 40934102.Google Scholar
Wall, E, Coffey, MP, Brotherstone, S 2007. The relationship between body energy traits and production and fitness traits in first-lactation dairy cows. Journal of Dairy Science 90, 15271537.Google Scholar
Wall, E, Coffey, MP, Amer, PR 2008. A theoretical framework for deriving direct economic values for body tissue mobilization traits in dairy cattle. Journal of Dairy Science 91, 343353.Google Scholar
Wall, E, Simm, G, Moran, D 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal 4, 366376.Google Scholar