Glossary
- AI or Artificial inseminatio:
-
deposition of bull semen into the reproductive tract of a female animal, usually after earlier semen collection, dilution, freezing, and storage and subsequent thawing, although some pasture-based systems that is with high seasonal demand use fresh semen. Allows elite males to produce many more offspring than by natural mating.
- Breeding goal/objective:
-
the set of traits which a breeding program is focused on to improve.
- Genomic selection:
-
selection of breeding animals based on the use of genetic markers (usually single nucleotide polymorphisms or SNPs that are equally spread across the genome) to estimate breeding values. The relationships among SNP genotypes and animal phenotypes are first estimated in a “reference population” of either males with large numbers of daughters with measurements, or cows with their own measurement, in order to estimate breeding values of selection candidates from genotypes only or a combination of genotypes and...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
References in the Text
Simm G, Pollott G, Mrode R, Houston R, Marshall K (2021) Genetic improvement of farmed animals. CABI, Wallingford. https://doi.org/10.1079/9781789241723.0000
Cole JB, Eaglen SAE, Maltecca C, Mulder HA, Pryce JE (2020) The future of phenomics in dairy cattle breeding. Anim Front 10(2):37–44. https://doi.org/10.1093/af/vfaa007
Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423–447. https://doi.org/10.2307/2529430
Mrode RA (1996) Linear models for prediction of animal breeding values. CAB INTERNATIONAL, Wallingford
Lucy M (2001) Reproductive loss in high-producing dairy cattle: where will it end? J Dairy Sci 84(6):1277–1293
Lucy MC (2019) Symposium review: selection for fertility in the modern dairy cow—Current status and future direction for genetic selection. J Dairy Sci 102(4):3706–3721. https://doi.org/10.3168/jds.2018-15544
Berry D, Wall E, Pryce J (2014) Genetics and genomics of reproductive performance in dairy and beef cattle. Anim: Int J Anim Biosci 8(s1):105–121
Rauw W, Kanis E, Noordhuizen-Stassen E, Grommers F (1998) Undesirable side effects of selection for high production efficiency in farm animals: a review. Livest Prod Sci 56(1):15–33
Boichard D, Brochard M (2012) New phenotypes for new breeding goals in dairy cattle. Anim Int J Anim Biosci 6(04):544–550
Martin-Collado D, Byrne T, Amer P, Santos B, Axford M, Pryce J (2015) Analyzing the heterogeneity of farmers’ preferences for improvements in dairy cow traits using farmer typologies. J Dairy Sci 98(6):4148–4161
Lopez-Villalobos N, Garrick DJ, Holmes CW, Blair HT, Spelman RJ (2000) Profitabilities of some mating systems for dairy herds in New Zealand. J Dairy Sci 83(1):144–153
Miglior F, Fleming A, Malchiodi F, Brito LF, Martin P, Baes CF (2017) A 100-year review: identification and genetic selection of economically important traits in dairy cattle. J Dairy Sci 100(12):10251–10271. https://doi.org/10.3168/jds.2017-12968
Mulder HA, Veerkamp RF, Ducro BJ, van Arendonk JAM, Bijma P (2006) Optimization of dairy cattle breeding programs for different environments with genotype by environment interaction. J Dairy Sci 89(5):1740–1752. https://doi.org/10.3168/jds.S0022-0302(06)72242-1
García-Ruiz A, Cole JB, VanRaden PM, Wiggans GR, Ruiz-López FJ, Van Tassell CP (2016) Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc Natl Acad Sci 113(28):E3995–E4004. https://doi.org/10.1073/pnas.1519061113
Goddard ME, Hayes BJ, Meuwissen TH (2010) Genomic selection in livestock populations. Genet Res 92(5–6):413–421. https://doi.org/10.1017/s0016672310000613
Hayes B, Bowman P, Chamberlain A, Goddard M (2009) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92(2):433–443
Hazel LN (1943) The genetic basis for constructing selection indexes. Genetics 28(6):476–490
Byrne T, Santos B, Amer P, Martin-Collado D, Pryce J, Axford M (2016) New breeding objectives and selection indices for the Australian dairy industry. J Dairy Sci 99:8146–8167
Walsh SW, Williams EJ, Evans ACO (2011) A review of the causes of poor fertility in high milk producing dairy cows. Anim Reprod Sci 123(3):127–138. https://doi.org/10.1016/j.anireprosci.2010.12.001
Dekkers JCM, Gibson JP (1998) Applying breeding objectives to dairy cattle improvement. J Dairy Sci 81:19–35. https://doi.org/10.3168/jds.S0022-0302(98)70151-1
Pryce JE, Veerkamp RF (2001) The incorporation of fertility indices in genetic improvement programmes. BSAP Occasional Publication 26(1):237–249
Veerkamp R, Mv P (2019) Advances in dairy cattle breeding to improve longevity. In: van der Werf J, Pryce J (eds) Advances in breeding of dairy cattle. Burleigh Dodds Science Publishing Limited, pp 337–354
Barkema HW, von Keyserlingk MAG, Kastelic JP, Lam TJGM, Luby C, Roy JP, LeBlanc SJ, Keefe GP, Kelton DF (2015) Invited review: changes in the dairy industry affecting dairy cattle health and welfare. J Dairy Sci 98(11):7426–7445. https://doi.org/10.3168/jds.2015-9377
Bell M, Eckard R, Pryce J (2012) Breeding dairy cows to reduce greenhouse gas emissions. IntechOpen
Ducrocq V, Quaas RL, Pollak EJ, Casella G (1988) Length of productive life of dairy cows. 1. Justification of a Weibull model. J Dairy Sci 71(11):3061–3070. https://doi.org/10.3168/jds.S0022-0302(88)79906-3
Veerkamp RF, Brotherstone S, Engel B, Meuwissen THE (2001) Analysis of censored survival data using random regression models. Anim Sci 72(1):1–10. https://doi.org/10.1017/S1357729800055491
van Pelt ML, Ducrocq V, de Jong G, Calus MPL, Veerkamp RF (2016) Genetic changes of survival traits over the past 25 yr in Dutch dairy cattle. J Dairy Sci 99(12):9810–9819. https://doi.org/10.3168/jds.2016-11249
Boettcher PJ, Jairath LK, Dekkers JCM (1999) Comparison of methods for genetic evaluation of sires for survival of their daughters in the first three lactations. J Dairy Sci 82(5):1034–1044. https://doi.org/10.3168/jds.S0022-0302(99)75324-5
Veerkamp RF, Hill WG, Stott AW, Brotherstone S, Simm G (1995) Selection for longevity and yield in dairy cows using transmitting abilities for type and yield. Anim Sci 61(2):189–197. https://doi.org/10.1017/S1357729800013710
Whay HR, Shearer JK (2017) The impact of lameness on welfare of the dairy cow. The veterinary clinics of North America. Food Anim Pract 33(2):153–164. https://doi.org/10.1016/j.cvfa.2017.02.008
Cole JB, VanRaden PM (2018) Symposium review: possibilities in an age of genomics: the future of selection indices1. J Dairy Sci 101(4):3686–3701. https://doi.org/10.3168/jds.2017-13335
Mrode R, Swanson G (1996) Genetic and statistical properties of somatic cell count and its suitability as an indirect means of reducing the incidence of mastitis in dairy cattle. Animal Breeding Abstracts (United Kingdom)
Abdelsayed M, Haile-Mariam M, Pryce JE (2017) Genetic parameters for health traits using data collected from genomic information nucleus herds. J Dairy Sci 100:9643–9655. https://doi.org/10.3168/jds.2017-12960
Pilla R, Malvisi M, Snel GGM, Schwarz D, König S, Czerny CP, Piccinini R (2013) Differential cell count as an alternative method to diagnose dairy cow mastitis. J Dairy Sci 96(3):1653–1660. https://doi.org/10.3168/jds.2012-6298
Norberg E (2005) Electrical conductivity of milk as a phenotypic and genetic indicator of bovine mastitis: a review. Livest Prod Sci 96(2):129–139
Soyeurt H, Colinet FG, Arnould VMR, Dardenne P, Bertozzi C, Renaville R, Portetelle D, Gengler N (2007) Genetic variability of lactoferrin content estimated by mid-infrared spectrometry in bovine milk. J Dairy Sci 90(9):4443–4450. https://doi.org/10.3168/jds.2006-827
Van der Waaij E, Holzhauer M, Ellen E, Kamphuis C, De Jong G (2005) Genetic parameters for claw disorders in Dutch dairy cattle and correlations with conformation traits. J Dairy Sci 88(10):3672–3678
Ødegård C, Svendsen M, Heringstad B (2013) Genetic analyses of claw health in Norwegian Red cows. J Dairy Sci 96(11):7274–7283
Buch LH, Sørensen AC, Lassen J, Berg P, Eriksson J-Å, Jakobsen J, Sørensen MK (2011) Hygiene-related and feed-related hoof diseases show different patterns of genetic correlations to clinical mastitis and female fertility. J Dairy Sci 94(3):1540–1551
Egger-Danner C, Nielsen P, Fiedler A, Müller K, Fjeldaas T, Döpfer D, Daniel V, Bergsten C, Cramer G, Christen A (2014) ICAR Claw Health Atlas. ICAR Technical Series (18)
Boelling D, Pollott G (1998) Locomotion, lameness, hoof and leg traits in cattle II: genetic relationships and breeding values. Livest Prod Sci 54(3):205–215
De la Paz JM (2008) Using antibody and cell-mediated immune response to test antigens in periparturient dairy cows as a measure of disease resistance. University of Florida
Thompson-Crispi K, Hine B, Quinton M, Miglior F, Mallard B (2012) Short communication: association of disease incidence and adaptive immune response in Holstein dairy cows. J Dairy Sci 95(7):3888–3893
Hernandez A, Quinton M, Miglior F, Mallard BA (2006) Genetic parameters of dairy cattle immune response traits. In: World Congress on Genetics Applied to Livestock Production: Belo Horizonte, MG, Brazil
Thompson-Crispi KA, Sewalem A, Miglior F, Mallard BA (2012) Genetic parameters of adaptive immune response traits in Canadian Holsteins. J Dairy Sci 95:401–409
Wilkie B, Mallard B (1999) Selection for high immune response: an alternative approach to animal health maintenance? Vet Immunol Immunopathol 72:231–235
De Marchi M, Toffanin V, Cassandro M, Penasa M (2014) Invited review: mid-infrared spectroscopy as phenotyping tool for milk traits. J Dairy Sci 97(3):1171–1186
Luke TDW, Rochfort S, Wales WJ, Bonfatti V, Marett L, Pryce JE (2019) Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra. J Dairy Sci 102(2):1747–1760. https://doi.org/10.3168/jds.2018-15103
Luke TDW, Nguyen TTT, Rochfort S, Wales WJ, Richardson CM, Abdelsayed M, Pryce JE (2019) Genomic prediction of serum biomarkers of health in early lactation. J Dairy Sci 102(12):11142–11152. https://doi.org/10.3168/jds.2019-17127
van den Berg I, Ho PN, Luke TDW, Haile-Mariam M, Bolormaa S, Pryce JE (2021) The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers. J Dairy Sci 104(2):2008–2017. https://doi.org/10.3168/jds.2020-19468
Colditz IG, Hine BC (2016) Resilience in farm animals: biology, management, breeding and implications for animal welfare. Anim Prod Sci 56:1961–1983
Rauw WM, Gomez-Raya L (2015) Genotype by environment interaction and breeding for robustness in livestock. Front Genet 6:310
Misztal I (2017) Breeding and genetics symposium: resilience and lessons from studies in genetics of heat stress. J Anim Sci 95:1780–1787
Wechsler B, Lea SEG (2007) Adaptation by learning: its significance for farmed aniaml husbandry. Appl Anim Behav Sci 108:197–214
Boissy A, Manteuffel G, Jensen MB, Moe RO, Spruijt B, Keeling LJ, Winckler C, Forkman B, Dimitrov I, Langbein J, Bakken M, Veissier I, Aubert A (2007) Assessment of positive emotions in animals to improve their welfare. Physiol Behav 92:375–397
Poppe M, Bonekamp G, van Pelt ML, Mulder HA (2021) Genetic analysis of resilience indicators based on milk yield records in different lactations and at different lactation stages. J Dairy Sci 104(2):1967–1981. https://doi.org/10.3168/jds.2020-19245
Carabaño MJ, Ramón M, Menéndez-Buxadera A, Molina A, Díaz C (2019) Selecting for heat tolerance. Anim Front 9(1):62–68. https://doi.org/10.1093/af/vfy033
Polsky L, von Keyserlingk MAG (2017) Invited review: effects of heat stress on dairy cattle welfare. J Dairy Sci 100(11):8645–8657. https://doi.org/10.3168/jds.2017-12651
Ravagnolo O, Misztal I (2000) Genetic component of heat stress in dairy cattle, parameter estimation. J Dairy Sci 83(9):2126–2130. https://doi.org/10.3168/jds.S0022-0302(00)75095-8
Nguyen TT, Bowman PJ, Haile-Mariam M, Pryce JE, Hayes BJ (2016) Genomic selection for tolerance to heat stress in Australian dairy cattle. J Dairy Sci 99(4):2849–2862
Nguyen TTT, Bowman PJ, Haile-Mariam M, Nieuwhof GJ, Hayes BJ, Pryce JE (2017) Short communication: implementation of a breeding value for heat tolerance in Australian dairy cattle. J Dairy Sci 100(9):7362–7367. https://doi.org/10.3168/jds.2017-12898
Cheruiyot EK, Haile-Mariam M, Cocks BG, MacLeod IM, Xiang R, Pryce JE (2021) New loci and neuronal pathways for resilience to heat stress in cattle. Sci Rep 11:16619. https://doi.org/10.1038/s41598-021-95816-8
Danielsson R, Dicksved J, Sun L, Gonda H, Müller B, Schnürer A, Bertilsson J (2017) Methane production in dairy cows correlates with rumen methanogenic and bacterial community structure. Front Microbiol 8:226. https://doi.org/10.3389/fmicb.2017.00226
de Haas Y, Pszczola M, Soyeurt H, Wall E, Lassen J (2017) Invited review: phenotypes to genetically reduce greenhouse gas emissions in dairying. J Dairy Sci 100(2):855–870. https://doi.org/10.3168/jds.2016-11246
Garnsworthy PC, Difford GF, Bell MJ, Bayat AR, Huhtanen P, Kuhla B, Lassen J, Peiren N, Pszczola M, Sorg D, Visker MHPW, Yan T (2019) Comparison of methods to measure methane for use in genetic evaluation of dairy cattle. Animals 9(10):837
Richardson CM, Nguyen TTT, Abdelsayed M, Moate PJ, Williams SRO, Chud TCS, Schenkel FS, Goddard ME, van den Berg I, Cocks BG, Marett LC, Wales WJ, Pryce JE (2021) Genetic parameters for methane emission traits in Australian dairy cows. J Dairy Sci 104(1):539–549. https://doi.org/10.3168/jds.2020-18565
Breider IS, Wall E, Garnsworthy PC (2019) Short communication: heritability of methane production and genetic correlations with milk yield and body weight in Holstein-Friesian dairy cows. J Dairy Sci 102(8):7277–7281. https://doi.org/10.3168/jds.2018-15909
Garnsworthy PC (2004) The environmental impact of fertility in dairy cows: a modelling approach to predict methane and ammonia emissions. Anim Feed Sci Technol 112(1):211–223. https://doi.org/10.1016/j.anifeedsci.2003.10.011
Bell M, Eckard R, Haile-Mariam M, Pryce J (2013) The effect of changing cow production and fitness traits on net income and greenhouse gas emissions from Australian dairy systems. J Dairy Sci 96(12):7918–7931
De Haas Y, Windig J, Calus M, Dijkstra J, De Haan M, Bannink A, Veerkamp R (2011) Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J Dairy Sci 94(12):6122–6134
Richardson CM, Amer PR, Hely FS, van den Berg I, Pryce JE (2021) Estimating methane coefficients to predict the environmental impact of traits in the Australian dairy breeding program. J Dairy Sci 104:10979–10990. https://doi.org/10.3168/jds.2021-20348
Wallace RJ, Sasson G, Garnsworthy PC, Tapio I, Gregson E, Bani P, Huhtanen P, Bayat AR, Strozzi F, Biscarini F, Snelling TJ, Saunders N, Potterton SL, Craigon J, Minuti A, Trevisi E, Callegari ML, Cappelli FP, Cabezas-Garcia EH, Vilkki J, Pinares-Patino C, Fliegerová KO, Mrázek J, Sechovcová H, Kopečný J, Bonin A, Boyer F, Taberlet P, Kokou F, Halperin E, Williams JL, Shingfield KJ, Mizrahi I (2019) A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci Adv 5(7):eaav8391. https://doi.org/10.1126/sciadv.aav8391
Johnson KA, Johnson DE (1995) Methane emissions from cattle. J Anim Sci 73(8):2483–2492. https://doi.org/10.2527/1995.7382483x
Berry D, Crowley J (2013) Cell biology symposium: genetics of feed efficiency in dairy and beef cattle. J Anim Sci 91(4):1594–1613
Pryce J, Gonzalez-Recio O, Nieuwhof G, Wales W, Coffey M, Hayes B, Goddard M (2015) Hot topic: definition and implementation of a breeding value for feed efficiency in dairy cows. J Dairy Sci 98:7340–7350
von Keyserlingk MAG, Weary DM (2017) A 100-year review: animal welfare in the Journal of Dairy Science—The first 100 years. J Dairy Sci 100(12):10432–10444. https://doi.org/10.3168/jds.2017-13298
Nielsen H-M, Christensen LG, Groen A (2005) Derivation of sustainable breeding goals for dairy cattle using selection index theory. J Dairy Sci 88(5):1882–1890
Brito LF, Oliveira HR, McConn BR, Schinckel AP, Arrazola A, Marchant-Forde JN, Johnson JS (2020) Large-scale phenotyping of livestock welfare in commercial production systems: a new Frontier in animal breeding. Front Genet 11:793–793. https://doi.org/10.3389/fgene.2020.00793
Bikker JP, van Laar H, Rump P, Doorenbos J, van Meurs K, Griffioen GM, Dijkstra J (2014) Technical note: evaluation of an ear-attached movement sensor to record cow feeding behavior and activity. J Dairy Sci 97(5):2974–2979. https://doi.org/10.3168/jds.2013-7560
McParland S, Kearney JF, Rath M, Berry DP (2007) Inbreeding effects on milk production, calving performance, fertility, and conformation in Irish Holstein-Friesians. J Dairy Sci 90(9):4411–4419. https://doi.org/10.3168/jds.2007-0227
Baes CF, Makanjuola BO, Miglior F, Marras G, Howard JT, Fleming A, Maltecca C (2019) Symposium review: the genomic architecture of inbreeding: how homozygosity affects health and performance. J Dairy Sci 102(3):2807–2817. https://doi.org/10.3168/jds.2018-15520
Kipp S, Segelke D, Schierenbeck S, Reinhardt F, Reents R, Wurmser C, Pausch H, Fries R, Thaller G, Tetens J, Pott J, Haas D, Raddatz BB, Hewicker-Trautwein M, Proios I, Schmicke M, Grünberg W (2016) Identification of a haplotype associated with cholesterol deficiency and increased juvenile mortality in Holstein cattle. J Dairy Sci 99(11):8915–8931. https://doi.org/10.3168/jds.2016-11118
Makanjuola BO, Miglior F, Abdalla EA, Maltecca C, Schenkel FS, Baes CF (2020) Effect of genomic selection on rate of inbreeding and coancestry and effective population size of Holstein and Jersey cattle populations. J Dairy Sci 103(6):5183–5199. https://doi.org/10.3168/jds.2019-18013
VanRaden PM, Olson KM, Wiggans GR, Cole JB, Tooker ME (2011) Genomic inbreeding and relationships among Holsteins, Jerseys, and Brown Swiss. J Dairy Sci 94(11):5673–5682. https://doi.org/10.3168/jds.2011-4500
Doublet A-C, Croiseau P, Fritz S, Michenet A, Hozé C, Danchin-Burge C, Laloë D, Restoux G (2019) The impact of genomic selection on genetic diversity and genetic gain in three French dairy cattle breeds. Genet Sel Evol 51(1):52. https://doi.org/10.1186/s12711-019-0495-1
Bjelland DW, Weigel KA, Vukasinovic N, Nkrumah JD (2013) Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding. J Dairy Sci 96(7):4697–4706. https://doi.org/10.3168/jds.2012-6435
Pryce J, Goddard M, Raadsma H, Hayes B (2010) Deterministic models of breeding scheme designs that incorporate genomic selection. J Dairy Sci 93(11):5455–5466
Pryce JE, Nguyen TTT, Axford M, Nieuwhof G, Shaffer M (2018) Symposium review: building a better cow: the Australian experience and future perspectives. J Dairy Sci 101(4):3702–3713. https://doi.org/10.3168/jds.2017-13377
Lasser J, Matzhold C, Egger-Danner C, Fuerst-Waltl B, Steininger F, Wittek T, Klimek P (2021) Integrating diverse data sources to predict disease risk in dairy cattle–a machine learning approach. J Anim Sci 99(11). https://doi.org/10.1093/jas/skab294
Books and Reviews (Further Reading)
The balanced breeding collection of papers in Journal of Dairy Science:
https://www.journalofdairyscience.org/content/balancedbreeding
This collection reviews the latest research on dairy cattle genetics:
van der Werf J, Pryce JE (2019) Advances in breeding of dairy cattle. Burleigh Dodds Science Publishing Limited
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2023 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
Cite this entry
Pryce, J.E. (2023). Dairy Cattle Breeding. In: Spangler, M.L. (eds) Animal Breeding and Genetics. Encyclopedia of Sustainability Science and Technology Series. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2460-9_1117
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2460-9_1117
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-0716-2459-3
Online ISBN: 978-1-0716-2460-9
eBook Packages: Biomedical and Life SciencesReference Module Biomedical and Life Sciences