Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-21T20:16:53.122Z Has data issue: false hasContentIssue false
  • English
  • Français

Invited review: Improving feed efficiency in dairy production: challenges and possibilities*

Published online by Cambridge University Press:  08 December 2014

E. E. Connor
E. E. Connor*
Affiliation:
Animal Genomics and Improvement Laboratory, US Department of Agriculture, Agricultural Research Service, 10300 Baltimore Avenue, Building 306, BARC-East, Beltsville, MD 20705, USA
*
†E-mail: erin.connor@ars.usda.gov
Get access

Abstract

Despite substantial advances in milk production efficiency of dairy cattle over the last 50 years, rising feed costs remain a significant threat to producer profitability. There also is a greater emphasis being placed on reducing the negative impacts of dairy production on the environment; thus means to lower greenhouse gas (GHG) emissions and nutrient losses to the environment associated with cattle production are being sought. Improving feed efficiency among dairy cattle herds offers an opportunity to address both of these issues for the dairy industry. However, the best means to assess feed efficiency and make genetic progress in efficiency-related traits among lactating cows without negatively impacting other economically important traits is not entirely obvious. In this review, multiple measurements of feed efficiency for lactating cows are described, as well as the heritability of the traits and their genetic and phenotypic correlations with other production traits. The measure of feed efficiency, residual feed intake is discussed in detail in terms of the benefits for its selection, how it could be assessed in large commercial populations, as well as biological mechanisms contributing to its variation among cows, as it has become a commonly used method to estimate efficiency in the recent scientific literature.

Keywords

dairy cowfeed efficiencyresidual feed intake
Type
Research Article
Information
animal , Volume 9 , Issue 3 , March 2015 , pp. 395 - 408
Copyright
© The Animal Consortium 2014 

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.)

Footnotes

*

The paper is based on an invited paper presented at the British Society of Animal Science Annual Meeting, Nottingham, April 2014.

References

Al-Husseini, W, Gondro, C, Quinn, K, Herd, RM, Gibson, JP and Chen, Y 2013. Expression of candidate genes for residual feed intake in Angus cattle. Animal Genetics 45, 12–19.Google Scholar
Arthur, PF, Renand, G and Krauss, D 2001. Genetic and phenotypic relationships among different measures of growth and feed efficiency in young Charolais bulls. Livestock Production Science 68, 131–139.Google Scholar
Bach, A 2012. Key indicators for measuring dairy cow performance. Proceedings of the FAO Symposium: Optimization of feed use efficiency in ruminant production systems, 27 November, Bangkok, Thailand, pp. 33–44.Google Scholar
Banos, G, Coffey, MP and Brotherstone, S 2005. Modeling daily energy balance of dairy cows in the first three lactations. Journal of Dairy Science 88, 2226–2237.Google Scholar
Banos, G, Coffey, MP, Veerkamp, RF, Berry, DP and Wall, E 2012. Merging and characterising phenotypic data on conventional and rare traits from dairy cattle experimental resources in three countries. Animal 6, 1040–1048.CrossRefGoogle ScholarPubMed
Barendse, W, Reverter, A, Bunch, RJ, Harrison, BE, Barris, W and Thomas, MB 2007. A validated whole-genome association study of efficient food conversion in cattle. Genetics 176, 1893–1905.Google Scholar
Basarab, JA, Colazo, MG, Ambrose, DJ, Novak, S, McCartney, D and Baron, VS 2011. Residual feed intake adjusted for backfat thickness and feeding frequency is independent of fertility in beef heifers. Canadian Journal of Animal Science 91, 573–584.Google Scholar
Basarab, JA, Beauchemin, KA, Baron, VS, Ominski, KH, Guan, LL, Miller, SP and Crowley, JJ 2013. Reducing GHG emissions through genetic improvement for feed efficiency: effects on economically important traits and enteric methane production. Animal 7 (suppl. 2), 303–315.Google Scholar
Bauman, DE and Currie, WB 1980. Partitioning of nutrients during pregnancy and lactation: a review of mechanisms involving homeostasis and homeorhesis. Journal of Dairy Science 63, 1514–1529.Google Scholar
Bell, MJ, Wall, E, Russell, G, Simm, G and Stott, AW 2012. The effect of improving cow productivity, fertility, and longevity on the global warming potential of dairy systems. Journal of Dairy Science 94, 3662–3678.Google Scholar
Berry, DP 2009. Improving feed efficiency in cattle with residual feed intake. In Recent advances in animal nutrition 2008 (ed. PC Garnsworthy and J Wiseman), pp. 67–99. Nottingham University Press, Nottingham, UK.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, 1594–1613.Google Scholar
Berry, DP, Coffey, MP, Pryce, JE, de Haas, Y, Løvendahl, P, Krattenmacher, N, Crowley, JJ, Wang, Z, Spurlock, D, Weigel, K, Macdonald, K and Veerkamp, RF 2014. International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources. Journal of Dairy Science 97, 3894–3905.Google Scholar
Black, TE, Bischoff, KM, Mercadante, VR, Marquezini, GH, Dilorenzo, N, Chase, CC Jr, Coleman, SW, Maddock, TD 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 Dairy Science 91, 2254–2263.Google Scholar
Bohmanova, J, Miglior, F, Jamrozik, J, Van Doormaal, BJ, Hand, KJ and Lazenby, D 2010. Genetic analysis of return over feed in Canadian Holsteins. Animal 4, 173–182.Google Scholar
Boichard, D and Brochard, M 2012. New phenotypes for new breeding goals in dairy cattle. Animal 6, 544–550.Google Scholar
Canadian Dairy Network 2014. Lifetime Profit Index (LPI) Formula, April 2013. Retrieved January 13, 2014, from http://www.cdn.ca/articles.php.Google Scholar
Capper, JL, Cady, RA and Bauman, DE 2009. The environmental impact of dairy production: 1944 compared with 2007. Journal of Animal Science 87, 2160–2167.Google Scholar
Carberry, CA, Kenny, DA, Han, S, McCabe, MS and Waters, SM 2012. Effect of phenotypic residual feed intake and dietary forage content on the rumen microbial community of beef cattle. Applied and Environmental Microbiology 78, 4949–4958.Google Scholar
Coleman, J, Berry, DP, Pierce, KM, Brennan, A and Horan, B 2010. Dry matter intake and feed efficiency profiles of 3 genotypes of Holstein–Friesian within pasture-based systems of milk production. Journal of Dairy Science 93, 4318–4331.Google Scholar
Collard, BL, Boettcher, PJ, Dekkers, JCM, Petitclerc, D and Schaeffer, LR 2000. Relationships between energy balance and health traits of dairy cattle in early lactation. Journal of Dairy Science 83, 2683–2690.Google Scholar
Connor, EE, Hutchison, JL and Norman, HD 2012a. Estimating feed efficiency of lactating dairy cattle using residual feed intake. In Feed efficiency in the beef industry (ed. RA Hill), pp. 159–174. Wiley-Blackwell, Oxford, UK.Google Scholar
Connor, EE, Hutchison, JL, Olson, KM and Norman, HD 2012b. Triennial lactation symposium: opportunities for improving milk production efficiency in dairy cattle. Journal of Animal Science 90, 1687–1694.Google Scholar
Connor, EE, Hutchison, JL, Norman, HD, Olson, KM, Van Tassell, CP, Leith, JM and Baldwin, RL 6th 2013. Use of residual feed intake in Holsteins during early lactation shows potential to improve feed efficiency through genetic selection. Journal of Animal Science 91, 3978–3988.Google Scholar
Cook, NB 2008. Time budgets for dairy cows: how does cow comfort influence health, reproduction and productivity? Proceedings of the Penn State Dairy Cattle Nutrition Workshop. 12–13 November, Grantville, PA, USA, pp. 53–60.Google Scholar
Crews, DH and Carstens, GE 2012. Measuring individual feed intake and utilization in growing cattle. In Feed efficiency in the beef industry (ed. RA Hill), pp. 21–28. Wiley-Blackwell, Oxford, UK.Google Scholar
Crowley, JJ, Evans, RD, Mc Hugh, N, Kenny, DA, McGee, M, Crews, DH Jr and Berry, DP 2011. Genetic relationships between feed efficiency in growing males and beef cow performance. Journal of Animal Science 89, 3372–3381.Google Scholar
Cruz, GD, Rodríguez-Sánchez, JA, Oltjen, JW and Sainz, RD 2010. Performance, residual feed intake, digestibility, carcass traits, and profitability of Angus-Hereford steers housed in individual or group pens. Journal of Animal Science 88, 324–329.Google Scholar
DairyNZ 2013. Technical Series April 2013. Issue 15. Retrieved December 13, 2013, from http://www.dairynz.co.nz/page/pageid/2145878009/Technical_Series#773.Google Scholar
Davis, SR, Macdonald, KA, Waghorn, GC and Spelman, RJ 2014. Residual feed intake of lactating Holstein–Friesian cows predicted from high-density genotypes and phenotyping of growing heifers. Journal of Dairy Science 97, 1436–1445.CrossRefGoogle ScholarPubMed
de Haas, Y, Windig, JJ, Calus, NPL, 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.Google Scholar
De Vries, MJ, Van Der Beek, S, Kaal-Lansbergen, LMTE, Ouweltjes, W and Wilmink, JBM 1999. Modeling of energy balance in early lactation and the effect of energy deficits in early lactation on first detected estrus postpartum in dairy cows. Journal of Dairy Science 82, 1927–1934.Google Scholar
Do, DN, Strathe, AB, Jensen, J, Mark, T and Kadarmideen, HN 2013. Genetic parameters for different measures of feed efficiency and related traits in boars of three pig breeds. Journal of Animal Science 91, 4069–4079.Google Scholar
Donohue, M and Cunningham, DL 2009. Effects of grain and oilseed prices on the costs of US poultry production. Journal of Applied Poultry Research 18, 325–337.Google Scholar
Emmerson, DA 1997. Commercial approaches to genetic selection for growth and feed conversion in domestic poultry. Poultry Science 76, 1121–1125.Google Scholar
Food and Agriculture Organization of the United Nations (FAOSTAT) 2013. Retrieved October 2, 2013, from http://faostat.fao.org/.Google Scholar
Goddard, M 2009. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–257.Google Scholar
Goddard, ME and Hayes, BJ 2007. Genomic selection. Journal of Animal Breeding and Genetics 124, 323–330.Google Scholar
Goff, JP and Horst, RL 1997. Physiological changes at parturition and their relationship to metabolic disorders. Journal of Dairy Science 80, 1260–1268.Google Scholar
Grainger, C, Clarke, T, McGinn, SM, Auldist, MJ, Beauchemin, KA, Hannah, MC, Waghorn, GC, Clark, H and Eckard, RJ 2007. Methane emissions from dairy cows measured using the sulfur hexafluoride (SF6) tracer and chamber techniques. Journal of Dairy Science 90, 2755–2766.CrossRefGoogle ScholarPubMed
Grant, R 2009. Stocking density and time budgets. Proceedings of the Western Dairy Management Conference, 11–13 March, Reno, NV, USA, pp. 7–17.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.Google Scholar
Guan, LL, Nkrumah, JD, Basarab, JA and Moore, SS 2009. Linkage of microbial ecology to phenotype: correlation of rumen microbial ecology to cattle’s feed efficiency. Federation of European Microbiological Societies Microbiology Letters 288, 85–91.Google Scholar
Gunsett, FC 1984. Linear index selection to improve traits defined as ratios. Journal of Animal Science 59, 1185–1193.Google Scholar
Hafla, AN, Carstens, GE, Forbes, TD, Tedeschi, LO, Bailey, JC, Walter, JT and Johnson, JR 2013. Relationships between postweaning residual feed intake in heifers and forage use, body composition, feeding behavior, physical activity, and heart rate of pregnant beef females. Journal of Animal Science 91, 5353–5365.Google Scholar
Hayes, BJ, Lewin, HA and Goddard, ME 2013. The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends in Genetics 29, 206–214.Google Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AJ and Goddard, ME 2009. Invited review: genomic selection in dairy cattle: Progress and challenges. Journal of Dairy Science 92, 433–443.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.Google Scholar
Herd, RM and Arthur, PF 2009. Physiological basis for residual feed intake. Journal of Animal Science 87 (E suppl.), E64–E71.Google Scholar
Herd, RM, Oddy, VH and Richardson, EC 2004. Biological basis for variation in residual feed intake in beef cattle. Australian Journal of Experimental Agriculture 44, 423–430.Google Scholar
Hernandez-Sanabria, E, Goonewardene, LA, Wang, Z, Durunna, ON, Moore, SS and Guan, LL 2012. Impact of feed efficiency and diet on adaptive variations in the bacterial community in the rumen fluid of cattle. Applied and Environmental Microbiology 78, 1203–1214.Google Scholar
Hernandez-Sanabria, E, Guan, LL, Goonewardene, LA, Li, M, Mujibi, DF, Stothard, P, Moore, SS and Leon-Quintero, MC 2010. Correlation of particular bacterial PCR-denaturing gradient gel electrophoresis patterns with bovine ruminal fermentation parameters and feed efficiency traits. Applied and Environmental Microbiology 76, 6338–6350.Google Scholar
Hietala, P, Wolfová, M, Wolf, J, Kantanen, J and Juga, J 2014. Economic values of production and functional traits, including residual feed intake, in Finnish milk production. Journal of Dairy Science 97, 1092–1106.Google Scholar
Hocking, PM 2010. Developments in poultry genetics research 1960–2009. British Poultry Science 51 (suppl. 1), 44–51.Google Scholar
Hou, Y, Bickhart, DM, Chung, H, Hutchison, JL, Norman, HD, Connor, EE and Liu, GE 2012. Analysis of copy number variations in Holstein cows identify potential mechanisms contributing to differences in residual feed intake. Functional and Integrative Genomics 12, 717–723.Google Scholar
Johnson, ZB, Chewning, JJ and Nugent, RA 3rd 1999. Genetic parameters for production traits and measures of residual feed intake in large white swine. Journal of Animal Science 77, 1679–1685.Google Scholar
Kelly, AK, McGee, M, Crews, DH Jr., Fahey, AG, Wylie, AR and Kenny, DA 2010. Effect of divergence in residual feed intake on feeding behavior, blood metabolic variables, and body composition traits in growing beef heifers. Journal of Animal Science 88, 109–123.Google Scholar
Koch, RM, Swiger, LA, Chambers, D and Gregory, KE 1963. Efficiency of food use in beef cattle. Journal of Animal Science 22, 486–494.Google 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.Google Scholar
Little, S, Linderman, J, Maclean, K and Janzen, H 2008. HOLOS – a tool to estimate and reduce greenhouse gases from farms. Methodology and algorithms for versions 1.1x, Agriculture and Agri-Food Canada, Cat. no. A52-136/2008E-PDF. Retrieved January 13, 2014, from http://publications.gc.ca/pub?id=342704&sl=0.Google Scholar
Littlejohn, M, Grala, T, Sanders, K, Walker, C, Waghorn, G, Macdonald, K, Spelman, R, Davis, S and Snell, R 2012. Non-replication of genome-wide-based associations of efficient food conversion in dairy cows. Animal Genetics 43, 781–784.Google Scholar
Liu, GE and Bickhart, DM 2012. Copy number variation in the cattle genome. Functional and Integrative Genomics 12, 609–624.Google Scholar
Macdonald, KA, Pryce, JE, Spelman, RJ, Davis, SR, Wales, WJ, Waghorn, GC, Williams, YJ, Marett, LC and Hayes, BJ 2014. Holstein–Friesian calves selected for divergence in residual feed intake during growth exhibited significant but reduced residual feed intake divergence in their first lactation. Journal of Dairy Science 97, 1427–1435.Google Scholar
Maher, AD, Hayes, B, Cocks, B, Marett, L, Wales, WJ and Rochfort, SJ 2013. Latent biochemical relationships in the blood−milk metabolic axis of dairy cows revealed by statistical integration of 1H NMR spectroscopic data. Journal of Proteome Research 12, 1428–1435.Google Scholar
Manafiazar, G, Miglior, F, Crews, D and Wang, Z 2012. Phenotypic and genetic correlations among feed efficiency, production and selected confirmation traits in dairy cattle – research progress report. Dairy Cattle Breeding and Genetics. Centre for Genetic Improvement of Livestock, Guelph, Canada, pp. 1–9.Google Scholar
Mc Geough, EJ, Little, SM, Janzen, HH, McAllister, TA, McGinn, SM and Beauchemin, KA 2012. Life-cycle assessment of greenhouse gas emissions from dairy production in Eastern Canada: a case study. Journal of Dairy Science 95, 5164–5175.Google Scholar
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
Mills, RE, Walter, K, Stewart, C, Handsaker, RE, Chen, K, Alkan, C, Abyzov, A, Yoon, SC, Ye, K, Cheetham, RK, Chinwalla, A, Conrad, DF, Fu, Y, Grubert, F, Hajirasouliha, I, Hormozdiari, F, Iakoucheva, LM, Iqbal, Z, Kang, S, Kidd, JM, Konkel, MK, Korn, J, Khurana, E, Kural, D, Lam, HYK, Leng, J, Li, R, Li, Y, Lin, CY, Luo, R, Mu, XJ, Nemesh, J, Peckham, HE, Rausch, T, Scally, A, Shi, X, Stromberg, MP, Stütz, AM, Urban, AE, Walker, J, Wu, J, Zhang, Y, Zhang, ZD, Batzer, M, Ding, L, Marth, GT, McVean, G, Sebat, J, Snyder, M, Wang, J, Ye, K, Eichler, EE, Gerstein, MB, Hurles, ME, Lee, C, McCarroll, S and Korbel, JO 2011. Mapping copy number variation by population-scale genome sequencing. Nature 470, 59–65.Google Scholar
Montanholi, YR, Swanson, KC, Palme, R, Schenkel, FS, McBride, BW, Lu, D and Miller, SP 2010. Assessing feed efficiency in beef steers through feeding behavior, infrared thermography and glucocorticoids. Animal 4, 692–701.Google Scholar
Moore, KL, Johnston, DJ, Graser, HU and Herd, RM 2005. Genetic and phenotypic relationships between insulin-like growth factor-I (IGF-I) and net feed intake, fat and growth traits in Angus beef cattle. Australian Journal of Experimental Agriculture 56, 211–218.Google Scholar
Moore, SS, Mujibi, FD and Sherman, EL 2009. Molecular basis for residual feed intake in beef cattle. Journal of Animal Science 87 (E suppl.), E41–E47.CrossRefGoogle ScholarPubMed
National Research Council (NRC) 2001. Nutrient requirements of dairy cattle, 7th revised edition. National Academy of Science, Washington, DC, USA.Google Scholar
Nieuwhof, GJ, van Arendonk, JAM, Vos, H and Korver, S 1992. Genetic relatinships between feed intake, efficiency and production traits in growing bulls, growing heifers and lactating heifers. Livestock Production Science 32, 189–202.Google 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
Nkrumah, JD, Sherman, EL, Li, C, Marques, E, Crews, DH Jr., Bartusiak, R, Murdoch, B, Wang, Z, Basarab, JA and Moore, SS 2007. Primary genome scan to identify putative quantitative trait loci for feedlot growth rate, feed intake, and feed efficiency of beef cattle. Journal of Animal Science 85, 3170–3181.Google Scholar
Oldenbroek, JK 1989. Parity effects on feed intake and feed efficiency in four dairy breeds fed ad libitum two different diets. Livestock Production Science 21, 115–129.Google Scholar
Prendiville, R, Pierce, KM and Buckley, F 2009. An evaluation of production efficiencies among lactating Holstein–Friesian, Jersey, and Jersey×Holstein-Friesian cows at pasture. Journal of Dairy Science 92, 6176–6185.Google Scholar
Pryce, JE, Wales, WJ, de Haas, Y, Veerkamp, RF and Hayes, BJ 2014b. Genomic selection for feed efficiency in dairy cattle. Animal 8, 1–10.Google Scholar
Pryce, JE, Gonzalez-Recio, O, Thornhill, JB, Marett, LC, Wales, WJ, Coffey, MP, de Haas, Y, Veerkamp, RF and Hayes, BJ 2014a. Short communication: validation of genomic breeding value predictions for feed intake and feed efficiency traits. Journal of Dairy Science 97, 537–542.Google Scholar
Pryce, JE, Arias, J, Bowman, PJ, Davis, SR, Macdonald, KA, Waghorn, GC, Wales, WJ, Williams, YJ, Spelman, RJ and Hayes, BJ 2012. Accuracy of genomic predictions of residual feed intake and 250-day body weight in growing heifers using 625,000 single nucleotide polymorphism markers. Journal of Dairy Science 95, 2108–2119.Google Scholar
Richardson, EC and Herd, RM 2004. Biological basis for variation in residual feed intake in beef cattle. 2. Synthesis of results following divergent selection. Australian Journal of Experimental Agriculture 44, 431–440.Google Scholar
Rius, AG, Kittelmann, S, Macdonald, KA, Waghorn, GC, Janssen, PH and Sikkema, E 2012. Nitrogen metabolism and rumen microbial enumeration in lactating cows with divergent residual feed intake fed high-digestibility pasture. Journal of Dairy Science 95, 5024–5034.Google Scholar
Rolf, MM, Taylor, JF, Schnabel, RD, McKay, SD, McClure, MC, Northcutt, SL, Kerley, MS and Weaber, RL 2012. Genome-wide association analysis for feed efficiency in Angus cattle. Animal Genetics 43, 367–374.Google Scholar
Saintilan, R, Mérour, I, Brossard, L, Tribout, T, Dourmad, JY, Sellier, P, Bidanel, J, van Milgen, J and Gilbert, H 2013. Genetics of residual feed intake in growing pigs: relationships with production traits, and nitrogen and phosphorus excretion traits. Journal of Animal Science 91, 2542–2554.Google Scholar
Schutz, M 2002. Dairy production. Retrieved January 20, 2014, from http://www.epa.gov/oecaagct/ag101/printdairy.html#lifetop.Google Scholar
Shaffer, KS, Turk, P, Wagner, WR and Felton, EE 2011. Residual feed intake, body composition, and fertility in yearling beef heifers. Journal of Animal Science 89, 1028–1034.Google Scholar
Sherman, EL, Nkrumah, JD, Li, C, Bartusiak, R, Murdoch, B and Moore, SS 2009. Fine mapping quantitative trait loci for feed intake and feed efficiency in beef cattle. Journal of Animal Science 87, 37–45.Google Scholar
Shook, GE 2006. Major advances in determining appropriate selection goals. Journal of Dairy Science 89, 1349–1361.Google Scholar
Smith, SN, Davis, ME and Loerch, SC 2010. Residual feed intake of Angus beef cattle divergently selected for feed conversion ratio. Livestock Science 132, 41–47.Google Scholar
Spurlock, DM, Dekkers, JCM, Fernando, R, Koltes, DA and Wolc, A 2012. Genetic parameters for energy balance, feed efficiency, and related traits in Holstein cattle. Journal of Dairy Science 95, 5393–5402.Google Scholar
Thoma, G, Popp, J, Shonnard, D, Nutter, D, Matlock, M, Ulrich, R, Kellogg, W, Kim, DS, Neiderman, Z, Kemper, N, Adomd, F and East, C 2013. Regional analysis of greenhouse gas emissions from USA dairy farms: a cradle to farm-gate assessment of the American dairy industry circa 2008. International Dairy Journal 31 (suppl. 1), S29–S40.Google Scholar
Trujillo, AI, Casal, A, Peñagaricano, F, Carriquiry, M and Chilibroste, P 2013. Association of SNP of neuropeptide Y, leptin, and IGF-I genes with residual feed intake in confinement and under grazing conditions in Angus cattle. Journal of Animal Science 91, 4235–4244.Google Scholar
US Department of Agriculture-Economic Research Service (USDA-ERS) 2013. Commodity costs and returns. Retrieved October 2, 2013, from http://www.ers.usda.gov/data-products.aspx.Google Scholar
US Environmental Protection Agency 2013. Inventory of US greenhouse gas emissions and sinks: 1990–2011. April 2013. USEPA #430-R-13-001. Retrieved October 2, 2013, from http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html.Google Scholar
Vallimont, JE, Dechow, CD, Daubert, JM, Dekleva, MW, Blum, JW, Liu, W, Varga, GA, Heinrichs, AJ and Baumrucker, CR 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, 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
VanRaden, PM 2004. Invited review: selection on net merit to improve lifetime profit. Journal of Dairy Science 87, 3125–3131.Google Scholar
VanRaden, PM, Van Tassell, CP, Wiggans, GR, Sonstegard, TS, Schnabel, RD, Taylor, JF and Schenkel, FS 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.Google Scholar
Veerkamp, R 2013. Breeding robust cows that produce healthier milk. Retrieved January 20, 2014, from http://blog.journals.cambridge.org/2013/09/breeding-robust-cows-that-produce-healthier-milk/.Google Scholar
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, 1109–1119.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.Google Scholar
Wattiaux, MA 2011. Heifer raising – weaning to calving, Chapter 33 Feeding and housing. In Dairy essentials (ed. Karen Nielsen), pp. 129–132. University of Wisconsin, Babcock Institute, Madison, WI, USA. Retrieved October 2, 2013, from http://babcock.wisc.edu/node/120.Google Scholar
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
Yao, C, Spurlock, DM, Armentano, LE, Page, CD Jr., VandeHaar, MJ, Bickhart, DM and Weigel, KA 2013. Random forests approach for identifying additive and epistatic single nucleotide polymorphisms associated with residual feed intake in dairy cattle. Journal of Dairy Science 96, 6716–6729.Google Scholar
Zamani, P, Miraei-Ashtiani, SR, Naserian, A, Nikkhan, A and Moradi-Shahrbabak, M 2005. Genetic variation of income over feed costs as an individual trait in Holstein cows. In Proceedings of the British Society Animal Science, 4–6 April, York, UK, p. 130.Google Scholar
Zhou, M, Hernandez-Sanabria, E and Guan, LL 2010. Characterization of variation in rumen methanogenic communities under different dietary and host feed efficiency conditions, as determined by PCR-denaturing gradient gel electrophoresis analysis. Applied and Environmental Microbiology 76, 3776–3786.Google Scholar