Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-23T03:50:13.210Z Has data issue: false hasContentIssue false
  • English
  • Français

Genomic selection for feed efficiency in dairy cattle

Published online by Cambridge University Press:  16 October 2013

J. E. Pryce ,
W. J. Wales ,
Y. de Haas ,
R. F. Veerkamp  and
B. J. Hayes
J. E. Pryce*
Affiliation:
Department of Environment and Primary Industries, Agribio, 5 Ring Road, La Trobe University, Bundoora 3083, Australia Dairy Futures CRC, Agribio, 5 Ring Road, La Trobe University, Bundoora 3083, Australia
W. J. Wales
Affiliation:
Dairy Futures CRC, Agribio, 5 Ring Road, La Trobe University, Bundoora 3083, Australia Department of Environment and Primary Industries, Ellinbank 3820, Australia
Y. de Haas
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200 AB Lelystad, The Netherlands
R. F. Veerkamp
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200 AB Lelystad, The Netherlands
B. J. Hayes
Affiliation:
Department of Environment and Primary Industries, Agribio, 5 Ring Road, La Trobe University, Bundoora 3083, Australia Dairy Futures CRC, Agribio, 5 Ring Road, La Trobe University, Bundoora 3083, Australia La Trobe University, Agribio, 5 Ring Road, Bundoora 3083, Australia
*
† E-mail: jennie.pryce@depi.vic.gov.au
Get access

Abstract

Feed is a major component of variable costs associated with dairy systems and is therefore an important consideration for breeding objectives. As a result, measures of feed efficiency are becoming popular traits for genetic analyses. Already, several countries account for feed efficiency in their breeding objectives by approximating the amount of energy required for milk production, maintenance, etc. However, variation in actual feed intake is currently not captured in dairy selection objectives, although this could be possible by evaluating traits such as residual feed intake (RFI), defined as the difference between actual and predicted feed (or energy) intake. As feed intake is expensive to accurately measure on large numbers of cows, phenotypes derived from it are obvious candidates for genomic selection provided that: (1) the trait is heritable; (2) the reliability of genomic predictions are acceptable to those using the breeding values; and (3) if breeding values are estimated for heifers, rather than cows then the heifer and cow traits need to be correlated. The accuracy of genomic prediction of dry matter intake (DMI) and RFI has been estimated to be around 0.4 in beef and dairy cattle studies. There are opportunities to increase the accuracy of prediction, for example, pooling data from three research herds (in Australia and Europe) has been shown to increase the accuracy of genomic prediction of DMI from 0.33 within country to 0.35 using a three-country reference population. Before including RFI as a selection objective, genetic correlations with other traits need to be estimated. Weak unfavourable genetic correlations between RFI and fertility have been published. This could be because RFI is mathematically similar to the calculation of energy balance and failure to account for mobilisation of body reserves correctly may result in selection for a trait that is similar to selecting for reduced (or negative) energy balance. So, if RFI is to become a selection objective, then including it in an overall multi-trait selection index where the breeding objective is net profit is sensible, as this would allow genetic correlations with other traits to be properly accounted for. If genetic parameters are accurately estimated then RFI is a logical breeding objective. If there is uncertainty in these, then DMI may be preferable.

Keywords

feed efficiencydairy cowgenomic selectionresidual feed intakedry matter intake
Type
Full Paper
Information
animal , Volume 8 , Issue 1 , January 2014 , pp. 1 - 10
Copyright
Copyright © The Animal Consortium 2013 

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

AFRC 1993. Energy and protein requirements of ruminants: an advisory manual prepared by the AFRC Technical Committee on responses to nutrients. CABI Publishing, Wallingford, UK.Google Scholar
Arthur, PF, Renand, G and Krauss, D 2001b. Genetic parameters for growth and feed efficiency in weaner versus yearling Charolais bulls. Crop and Pasture Science 52, 471–476.CrossRefGoogle Scholar
Arthur, PF, Archer, JA, Johnston, DJ, Herd, RM, Richardson, EC and Parnell, PF 2001a. Genetic and phenotypic variance and covariance components for feed intake, feed efficiency, and other postweaning traits in Angus cattle. Journal of Animal Science 79, 2805–2811.CrossRefGoogle ScholarPubMed
Banos, G and Coffey, MP 2012. Technical note: prediction of liveweight from linear conformation traits in dairy cattle. Journal of Dairy Science 95, 2170–2175.Google Scholar
Bastin, C, Loker, S, Gengler, N, Sewalem, A and Miglior, F 2010. Genetic relationships between body condition score and reproduction traits for Canadian Holstein and Ayrshire first-parity cows. Journal of Dairy Science 93, 2215–2228.CrossRefGoogle ScholarPubMed
Bell, MJ, Eckard, RJ, Haile-Mariam, M and Pryce, JE 2013. The effect of improving cow production and fitness traits on net income and greenhouse gas emissions from Australian dairy systems. Journal of Dairy Science (submitted).CrossRefGoogle Scholar
Berry, DP and Crowley, JJ 2013. Genetics of feed efficiency in dairy and beef cattle. Journal of Animal Science 91, 1594–1613.Google Scholar
Berry, DP, Buckley, F, Dillon, P, Evans, RD, Rath, M and Veerkamp, RF 2003. Genetic relationships among body condition score, body weight, milk yield, and fertility in dairy cows. Journal of Dairy Science 86, 2193–2204.CrossRefGoogle ScholarPubMed
Black, TE, Bischoff, KM, Mercadante, VRG, Marquezini, GHL, Dilorenzo, N, Chase, CC and Lamb, GC 2013. Relationships among performance, residual feed intake, and temperament assessed in growing beef heifers and subsequently as 3-year-old, lactating beef cows. Journal of Animal Science 91, 2254–2263.Google Scholar
Bolormaa, S, Pryce, JE, Kemper, K, Savin, K, Hayes, BJ, Barendse, W, Reverter, A, Herd, RM, Zhang, Y, Tier, B and Goddard, ME 2013. Prediction of genomic breeding values in Beef cattle. Journal of Animal Science 91, 3088–3104.CrossRefGoogle ScholarPubMed
Coffey, MP, Simm, G and Brotherstone, S 2002. Energy balance profiles for the first three lactations of dairy cows estimated using random regression. Journal of Dairy Science 85, 2669–2678.CrossRefGoogle ScholarPubMed
Crowley, JJ, McGee, M, Kenny, DA, Crews, DH Jr, Evans, RD and Berry, DP 2010. Phenotypic and genetic parameters for different measures of feed efficiency in different breeds of Irish performance-tested bulls. Journal of Animal Science 88, 885–894.CrossRefGoogle Scholar
CSIRO 2007. Nutrient requirements of sdomesticated ruminants. CSIRO Publishing, Melbourne, Victoria, Australia.Google Scholar
Daetwyler, HD, Villanueva, B and Woolliams, JA 2008. Accuracy of predicting genetic risk of disease using a genome-wide approach. PLoS One 3, e3395. http://doi:10.1371/journal.pone.0003395.CrossRefGoogle ScholarPubMed
De Haas, Y, Calus, MPL, Veerkamp, RF, Wall, E, Coffey, MP, Daetwyler, HD, Hayes, BJ and Pryce, JE 2012. Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets. Journal of Dairy Science 95, 6103–6112.CrossRefGoogle ScholarPubMed
De Haas, Y, Windig, JJ, Calus, MPL, Dijkstra, J, de Haan, M, Bannink, A and Veerkamp, RF 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. Journal of Dairy Science 94, 6122–6134.CrossRefGoogle Scholar
Dechow, CD, Rogers, GW, Klei, L, Lawlor, TJ and VanRaden, PM 2004. Body condition scores and dairy form evaluations as indicators of days open in US Holsteins. Journal of Dairy Science 87, 3534–3541.Google Scholar
Durunna, ON, Mujibi, FD, Nkrumah, DJ, Basarab, JA, Okine, EK, Moore, SS and Wang, Z 2013. Genetic parameters for production and feeding behaviour traits in crossbred steers fed a finishing diet at different ages. Canadian Journal of Animal Science 93, 79–87.CrossRefGoogle Scholar
Erbe, M, Hayes, BJ, Matukumalli, LK, Goswami, S, Bowman, PJ, Reich, CM, Mason, BA and Goddard, ME 2012. Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. Journal of Dairy Science 95, 4114–4129.Google Scholar
Fan, LQ, Bailey, DRC and Shannon, NH 1995. Genetic parameter estimation of postweaning gain, feed intake and efficiency for Hereford and Angus bulls fed two different diets. Journal of Animal Science 73, 365–372.CrossRefGoogle ScholarPubMed
Gibson, MP 1986. Efficiency and performance of genetically high and low milk-producing British Friesian and Jersey cattle. Animal Production 42, 161–182.Google Scholar
Green, TC, Jago, JG, Macdonald, KA and Waghorn, GC 2013. Relationships between residual feed intake, average daily gain, and feeding behavior in growing dairy heifers. Journal of Dairy Science 96, 3098–3107.CrossRefGoogle ScholarPubMed
Harris, BL, Pryce, JE and Montgomerie, WA 2007. Experiences from breeding for economic efficiency in dairy cattle in New Zealand. Proceedings for the Advancement of Animal Breeding and Genetics 17, 434–444.Google Scholar
Hayes, BJ, van der Werf, JHJ and Pryce, JE 2011. Economic benefit of genomic selection for residual feed intake (as a measure of feed conversion efficiency) in Australian dairy cattle. Recent Advances in Animal Nutrition 18, 31–35.Google Scholar
Hayes, BJ, Lewin, HA and Goddard, ME 2012. The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity and adaptation. Trends in Genetics 29, 206–214.Google Scholar
Hegarty, RS, Goopy, JP, Herd, RM and McCorkell, B 2007. Cattle selected for lower residual feed intake have reduced daily methane production. Journal of Animal Science 85, 1479–1486.CrossRefGoogle ScholarPubMed
Herd, RM and Bishop, SC 2000. Genetic variation in residual feed intake and its association with other production traits in British Hereford cattle. Livestock Production Science 63, 111–119.Google Scholar
Herd, RM, Oddy, VH and Richardson, EC 2004. Biological basis for variation in residual feed intake in beef cattle 1. Review of potential mechanisms. Australian Journal of Experimental Agriculture 44, 423–430.CrossRefGoogle Scholar
Johnston, DJ, Barwick, SA, Corbet, NJ, Fordyce, G, Holroyd, RG, Williams, PJ and Burrow, HM 2009. Genetics of heifer puberty in two tropical beef genotypes in northern Australia and associations with heifer-and steer-production traits. Animal Production Science 49, 399–412.CrossRefGoogle Scholar
Jones, FM, Phillips, FA, Naylor, T and Mercer, NB 2011. Methane emissions from grazing Angus beef cows selected for divergent residual feed intake. Animal Feed Science and Technology 166, 302–307.Google Scholar
Jones, HE, Warkup, CC, Williams, A and Audsley, E 2008. The effect of genetic improvement on emission from livestock systems. In Proceedings of the European Association of Animal Production, 24–27 August, Vilnius, Lithuania, p. 28.Google Scholar
Khansefid, M, Pryce, JE, Miller, SP and Goddard, ME 2013. Accuracy of genomic prediction for residual feed intake in a multi-breed populatio. Paper presented at the 20th Association for the Advancement of Animal Breeding and Genetics conference, Napier, 20 to 23 October 2013, Napier, New Zealand.Google Scholar
Koch, RM, Swiger, LA, Chambers, D and Gregory, KE 1963. Efficiency in beef cattle. Journal of Animal Science 22, 486–494.CrossRefGoogle Scholar
Koenen, EPC and Veerkamp, RF 1998. Genetic covariance functions for live weight, condition score, and dry-matter intake measured at different lactation stages of Holstein Friesian heifers. Livestock Production Science 57, 67–77.CrossRefGoogle Scholar
Korver, S 1988. Genetic aspects of feed intake and feed efficiency in dairy cattle: a review. Livestock Production Science 20, 1–13.Google Scholar
Korver, S, van Eekelen, EAM, Vos, H, Nieuwhof, GJ and van Arendonk, JAM 1991. Genetic parameters for feed intake and feed efficiency in growing dairy heifers. Livestock Production Science 29, 49–59.CrossRefGoogle Scholar
Lin, Z, Macleod, I and Pryce, JE 2013. Short communication: estimation of genetic parameters for residual feed intake and feeding behavior traits in dairy heifers. Journal of Dairy Science 96, 2654–2656.CrossRefGoogle ScholarPubMed
Loker, S, Bastin, C, Miglior, F, Sewalem, A, Schaeffer, LR, Jamrozik, J and Osborne, V 2011. Short communication: estimates of genetic parameters of body condition score in the first 3 lactations using a random regression animal model. Journal of Dairy Science 94, 3693–3699.CrossRefGoogle ScholarPubMed
Mao, F, Chen, L, Vinsky, M, Okine, E, Wang, Z, Basarab, J, Crews, DH Jr and Li, C 2013. Phenotypic and genetic relationships of feed efficiency with growth performance, ultrasound, and carcass merit traits in Angus and Charolais steers. Journal of Animal Science 91, 2067–2076.CrossRefGoogle ScholarPubMed
Meuwissen, THE, Hayes, BJ and Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.Google Scholar
Mujibi, FDN, Nkrumah, JD, Durunna, ON, Stothard, P, Mah, J, Wang, Z, Basarab, J, Plastow, G, Crews, DH Jr and Moore, SS 2011. Accuracy of genomic breeding values for residual feed intake in beef cattle. Journal of Animal Science 89, 3353–3361.CrossRefGoogle ScholarPubMed
Ngwerume, F and Mao, IL 1992. Estimation of residual energy intake for lactating cows using an animal model. Journal of Dairy Science 75, 2283–2287.Google Scholar
Nieuwhof, GJ, Van Arendonk, JAM, Vos, H and Korver, S 1992. Genetic relationships between feed intake, efficiency and production traits in growing bulls, growing heifers and lactating heifers. Livestock Production Science 32, 189–202.CrossRefGoogle Scholar
Nkrumah, JD, Crews, DH, Basarab, JA, Price, MA, Okine, EK, Wang, Z, Li, C and Moore, SS 2007. Genetic and phenotypic relationships of feeding behavior and temperament with performance, feed efficiency, ultrasound, and carcass merit of beef cattle. Journal of Animal Science 89, 2382–2390.CrossRefGoogle Scholar
Nkrumah, JD, Okine, EK, Mathison, GW, Schmid, K, Li, C, Basarab, JA, Price, MA, Wang, Z and Moore, SS 2006. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. Journal of Animal Science 84, 145–153.Google Scholar
National Research Council (NRC) 2001. Nutrient requirements of dairy cattle, 7th revised edition. National Academy Press, Washington, DC, USA.Google Scholar
Pryce, JE and Harris, BL 2006. Genetics of body condition score in New Zealand dairy cattle. Journal of Dairy Science 89, 4424–4432.CrossRefGoogle Scholar
Pryce, JE, Coffey, MP and Simm, G 2001. The relationship between body condition score and reproductive performance. Journal of Dairy Science 84, 1508–1515.CrossRefGoogle ScholarPubMed
Pryce, JE, Harris, BL, Johnson, DL and Montgomerie, WA 2006. Body condition score as a candidate trait in the breeding worth dairy index. Proceedings of the New Zealand. Society of Animal Production 66, pp. 103–106.Google Scholar
Pryce, JE, Arias, J, Bowman, PJ, Davis, SR, Macdonald, KA, Waghorn, GC, Wales, WJ, Williams, YJ, Spelman, RJ and Hayes, BJ 2012a. Accuracy of genomic predictions of residual feed intake and 250 day bodyweight in growing heifers using 625,000 SNP markers. Journal of Dairy Science 95, 2108–2119.Google Scholar
Pryce, JE, Marett, L, Wales, WJ, Williams, YJ and Hayes, BJ 2012b. Calves selected for divergence in feed conversion efficiency for growth also exhibit divergence in feed conversion efficiency in lactation. In Proceedings of the Australian Dairy Science Symposium, 13 to 15 November 2012, Melbourne, Australia, pp. 45–47.Google Scholar
Pryce, JE, Gonzalez-Recio, O, Thornhill, JB, Marett, LC, Wales, WJ, Coffey, MP, de Haas, Y, Veerkamp, RF and Hayes, BJ 2013. Short Communication: Validation of genomic breeding value predictions for feed intake and feed efficiency traits. Journal of Dairy Science (accepted 26th September, 2013).Google Scholar
Roche, JR, Friggens, NC, Kay, JK, Fisher, MW, Stafford, KJ and Berry, DP 2009. Invited review: body condition score and its association with dairy cow productivity, health, and welfare. Journal of Dairy Science 92, 5769–5801.Google Scholar
Svendsen, M, Skipenes, P and Mao, IL 1993. Genetic parameters in the feed conversion complex of primiparous cows in the first two trimesters. Journal of Animal Science 71, 1721–1729.Google Scholar
Vallimont, JE, Dechow, CD, Daubert, JM, Dekleva, MW, Blum, JW, Liu, W, Varga, GA, Heinrichs, AJ and Baumrucker, CR 2012. Short communication: feed utilization and its associations with fertility and productive life in 11 commercial Pennsylvania tie-stall herds. Journal of Dairy Science 96, 1251–1254.Google Scholar
Vallimont, JE, Dechow, CD, Daubert, JM, Dekleva, MW, Blum, JW, Barlieb, CM, Liu, W, Varga, GA, Heinrichs, AJ and Baumrucker, CR 2011. Short communication: heritability of gross feed efficiency and associations with yield, intake, residual intake, body weight, and body condition score in 11 commercial Pennsylvania tie stalls. Journal of Dairy Science 94, 2108–2113.Google Scholar
Van Arendonk, JAM, Nieuwhof, GJ, Vos, H and Korver, S 1991. Genetic aspects of feed intake and efficiency in lactating dairy heifers. Livestock Production Science 29, 263–275.Google Scholar
Van der Steen, HAM, Prall, GFW and Plastow, GS 2005. Application of genomics to the pork industry. Journal of Animal Science 83, E1–E8.Google Scholar
VanRaden, PM 2004. Invited review: selection on net merit to improve lifetime profit. Journal of Dairy Science 87, 3125–3131.Google Scholar
VanRaden, PM 2009. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.Google Scholar
Veerkamp, RF 1998. Selection for economic efficiency of dairy cattle using information on liveweight and feed intake: a review. Journal of Dairy Science 81, 1109–1119.Google Scholar
Veerkamp, RF and Brotherstone, S 1997. Genetic correlations between linear type traits, food intake, live weight and condition score in Holstein Friesian dairy cattle. Animal Science 64, 385–392.CrossRefGoogle Scholar
Veerkamp, RF, Emmans, GC, Cromie, AR and Simm, G 1995. Variance components for residual feed intake in dairy cows. Livestock Production Science 41, 111–120.CrossRefGoogle Scholar
Veerkamp, RF, Oldenbroek, JK, Van der Gaast, HJ and 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 83, 577–583.Google Scholar
Verbyla, KL, Hayes, BJ, Bowman, PJ and Goddard, ME 2009. Accuracy of genomic selection using stochastic search variable selection in Australian Holstein Friesian dairy cattle. Genetics Research 91, 307–311.Google Scholar
Verbyla, KL, Calus, MPL, Mulder, HA, De Haas, Y and Veerkamp, RF 2010. Predicting energy balance for dairy cows using high-density single nucleotide polymorphism information. Journal of Dairy Science 93, 2757–2764.Google Scholar
Waghorn, GC, Macdonald, KA, Williams, Y, Davis, SR and Spelman, RJ 2012. Measuring residual feed intake in dairy heifers fed an alfalfa Medicago sativa cube diet. Journal of Dairy Science 95, 1462–1471.CrossRefGoogle ScholarPubMed
Williams, YJ, Pryce, JE, Grainger, C, Wales, WJ, Linden, N, Porker, M and Hayes, BJ 2011. Variation in residual feed intake in Holstein-Friesian dairy heifers in southern Australia. Journal of Dairy Science 94, 4715–4725.Google Scholar
Yan, T, Mayne, CS, Gordon, FG, Porter, MG, Agnew, RE, Patterson, DC, Ferris, CP and Kilpatrick, DJ 2010. Mitigation of enteric methane emissions through improving efficiency of energy utilization and productivity in lactating dairy cows. Journal of Dairy Science 93, 2630–2638.CrossRefGoogle ScholarPubMed
Yang, J, Benyamin, B, McEvoy, NP, Gordon, S, Henders, AK, Nyholt, DR, Madden, PA, Heath, AC, Martin, NG, Montgomery, GW, Goddard, ME and Visscher, PM 2010. Common SNPs explain a large proportion of the heritability for human height. Nature Genetics 42, 565–569.Google Scholar