Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-22T19:04:13.217Z Has data issue: false hasContentIssue false
  • English
  • Français

Determining the economic value of daily dry matter intake and associated methane emissions in dairy cattle

Published online by Cambridge University Press:  22 July 2019

C. M. Richardson Open the ORCID record for C. M. Richardson [Opens in a new window] ,
C. F. Baes ,
P. R. Amer ,
C. Quinton ,
P. Martin ,
V. R. Osborne ,
J. E. Pryce  and
F. Miglior
C. M. Richardson*
Affiliation:
Center for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1Canada
C. F. Baes
Affiliation:
Center for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1Canada
P. R. Amer
Affiliation:
AbacusBio Limited, PO Box 5585, Dunedin, New Zealand
C. Quinton
Affiliation:
AbacusBio Limited, PO Box 5585, Dunedin, New Zealand
P. Martin
Affiliation:
Center for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1Canada
V. R. Osborne
Affiliation:
Center for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1Canada
J. E. Pryce
Affiliation:
Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria 3083, Australia School of Applied Systems Biology, La Trobe University, 5 Ring Road, Bundoora, Victoria 3083, Australia
F. Miglior
Affiliation:
Center for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1Canada Canadian Dairy Network, 660 Speedvale Avenue West, Guelph, ON, N1K 1E5, Canada
*
†E-mail: [email protected]
Get access

Abstract

Feed represents a substantial proportion of production costs in the dairy industry and is a useful target for improving overall system efficiency and sustainability. The objective of this study was to develop methodology to estimate the economic value for a feed efficiency trait and the associated methane production relevant to Canada. The approach quantifies the level of economic savings achieved by selecting animals that convert consumed feed into product while minimizing the feed energy used for inefficient metabolism, maintenance and digestion. We define a selection criterion trait called Feed Performance (FP) as a 1 kg increase in more efficiently used feed in a first parity lactating cow. The impact of a change in this trait on the total lifetime value of more efficiently used feed via correlated selection responses in other life stages is then quantified. The resulting improved conversion of feed was also applied to determine the resulting reduction in output of emissions (and their relative value based on a national emissions value) under an assumption of constant methane yield, where methane yield is defined as kg methane/kg dry matter intake (DMI). Overall, increasing the FP estimated breeding value by one unit (i.e. 1 kg of more efficiently converted DMI during the cow’s first lactation) translates to a total lifetime saving of 3.23 kg in DMI and 0.055 kg in methane with the economic values of CAD $0.82 and CAD $0.07, respectively. Therefore, the estimated total economic value for FP is CAD $0.89/unit. The proposed model is robust and could also be applied to determine the economic value for feed efficiency traits within a selection index in other production systems and countries.

Keywords

feed efficiencyenvironmental impacteconomic weightselection indexanimal breeding
Type
Research Article
Information
animal , Volume 14 , Issue 1 , January 2020 , pp. 171 - 179
Copyright
© The Animal Consortium 2019 

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

Alqaisi, O, Ndambi, A and Hemme, T 2011. Global view on feed cost and feed efficiency on dairy farms. All About Feed 2, 12–14.Google Scholar
Amer, PR, Hely, FS, Quinton, CD and Cromie, AR 2018. A methodology framework for weighting genetic traits that impact greenhouse gas emissions intensity into selection indexes. Animal 12, 5–11. doi: 10.1017/S1751731117001549 CrossRefGoogle ScholarPubMed
Bank of Canada 2016. Bank of Canada statistics. Retrieved on 3 July 2016 from https://www.bankofcanada.ca/rates/ Google Scholar
Basarab, JA, Beauchemin, KA, Baron, VS, Ominski, KH, Guan, LL, Miller, SP and Crowley, JJ. 2013. Reducing GHG emission through genetic improvement for feed efficiency: effects on economically important traits and enteric methane production. Animal 7, 303–315. doi: 10.1017/S1751731113000888 Google Scholar
Bell, MJ, Pryce, J and Wilson, P 2016. A comparison of the economic value for enteric methane emissions with other biological traits associated with dairy cows. American Research Journal of Agriculture 2, 1–17. doi: 10.21694/2379-1047.16002 Google Scholar
Bell, MJ, Wall, E, Simm, G and Russell, G 2011. Effects of genetic line and feeding system on methane emissions from dairy systems. Animal Feed Science and Technology 167, 699–707. doi: 10.1016/j.anifeedsci.2011.04.049 CrossRefGoogle 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. doi: 10.3168/jds.2013-7548 Google ScholarPubMed
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. doi:10.2527/jas.2012-5862 Google ScholarPubMed
Berry, DP, Madalena, FE, Cromie, AR and Amer, PR 2006. Cumulative discounted expression of dairy and beef traits in cattle production systems. Livestock Science 99, 159–174 Google Scholar
Canadian Dairy Commission 2016. Ontario dairy farm accounting project reports (2005–2015). Retrieved on 3 July 2016 from https://www.milk.org/corporate/pdf/Publications-ODFAPReport.pdf Google Scholar
Canadian Dairy Network (CDN) 2016. Selection for increased resistance to metabolic diseases. Retrieved on 3 July 2016 from https://www.cdn.ca/articles.php 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. doi:10.3168/jds.2009-2686 CrossRefGoogle ScholarPubMed
Connor, EE 2015. Invited review: improving feed efficiency in dairy production: challenges and possibilities. Animal 9, 395–408. doi: 10.1017/S1751731114002997 Google ScholarPubMed
CRV 2016. Breeding value dry matter intake. Retrieved on 3 July 2016 from https://www.crv4all-international.com/wp-content/uploads/2016/04/E_40_apr2016_eng.pdf Google Scholar
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. doi:10.3168/jds.2011-4439.CrossRefGoogle Scholar
de Jong, G, van der Linde, R, de Haas, Y, Schopen, GCB and Veerkamp, RF 2016. Genetic evaluation for feed intake in the Netherlands and Flanders, impact on efficiency and responses. In Proceedings of the 50th Interbull Annual Meeting, 24–28 October 2016, Puerto Varas, Chile, pp. 1–4. Google Scholar
Environment Canada 2016. The Paris agreement. Retrieved on 3 July 2016 from https://www.canada.ca/en/environment-climate-change/services/climate-change/paris-agreement.html Google Scholar
FAO 2006. Livestock long shadow: environmental issues and options. Retrieved on 10 July 2016 from http://www.fao.org/3/a-a0701e.pdfGoogle 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, 3883–3893. doi: 10.3168/jds.2013-7515 Google ScholarPubMed
Hagemann, M., Hemme, T, Ndambi, A, Alqaisi, O and Sultana, MN 2011. Benchmarking of greenhouse gas emissions of bovine milk production systems for 38 countries. Animal Feed Science and Technology 166, 46–58. doi: 10.1016/j.anifeedsci.2011.04.002 CrossRefGoogle 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. doi: 10.2527/jas.2006-236 CrossRefGoogle ScholarPubMed
Hemme, T, Uddin, MM and Ndambi, OA 2014. Benchmarking cost of milk production in 46 countries. Journal of Reviews on Global Economics 3, 254–270 Google Scholar
Herrero, M, Henderson, B, Havlík, P, Thornton, PK, Conant, RT, Smith, P, Wirsenius, S, Hristov, AN, Gerber, P, Gill, M, Butterbach-Bahl, K, Valin, H, Garnett, T and Stehfest, E 2016. Greenhouse gas mitigation potentials in the livestock sector. Nature Climate Change 6, 452–461. doi: 10.1038/nclimate2925 CrossRefGoogle Scholar
Hurley, AM, López-Villalobos, N, McParland, S, Lewis, E, Kennedy, E, O’Donovan, M, Burke, JL and Berry, DP 2017. Genetics of alternative definitions of feed efficiency in grazing lactating dairy cows. Journal of Dairy Science 100, 5501–5514. doi:10.3168/jds.2016-12314 CrossRefGoogle ScholarPubMed
Kennedy, BW, van der Werf, JH and Meuwissen, TH 1993. Genetic and statistical properties of residual feed intake. Journal of Animal Science 71, 3239–3250. doi:10.2527/1993.71123239x CrossRefGoogle ScholarPubMed
Lassen, J and Løvendahl, P 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. Journal of Dairy Science 99, 1959–1967. doi: 10.3168/jds.2015-10012 CrossRefGoogle Scholar
Li, B, Fikse, WF, Lassen, J, Lidauer, MH, Løvendahl, P, Mäntysaari, P and Berglund, B 2016. Genetic parameters for dry matter intake in primiparous Holstein, Nordic Red, and Jersey cows in the first half of lactation. Journal of Dairy Science 99, 7232–7239. doi:10.3168/jds.2015-10669 CrossRefGoogle ScholarPubMed
Menajovsky, SB, Walpole, CE, DeVries, TJ, Schwartzkopf-Genswein, KS, Walpole, ME and Penner, GB 2018. The effect of the forage-to-concentrate ratio of the partial mixed ration and the quantity of concentrate in an automatic milking system for lactating Holstein cows. Journal of Dairy Science 101, 9941–9953. doi: 10.3168/jds.2018-14665 Google Scholar
Meyer, S., Amer, PR, Baes, C, Miglior, F, Richardson, C, Wall, E and Coffey, M 2017. Options for incorporating feed intake into national selection indexes. In Proceedings of the 51st Interbull Annual Meeting, 25–28 August 2017, Tallinn, Estonia, pp. 48–56.Google Scholar
Miglior, F, Finocchiaro, R, Malchiodi, F, Fleming, A, Brito, L, Baes, CF, Jamrozik, J, Martin, P, Chesnais, J and Schenke, Fl 2016. Enhancing the data pipeline for novel traits in the genomic era: from farms to DHI to evaluation centres. In Proceeding of the 40th International Committee for Animal Recording Biennial Session, 24–28 October 2016, Puerto Varas, Chile, pp. 23–30.Google Scholar
Miglior, F, Fleming, A, Malchiodi, F, Brito, L, Martin, P and Baes, CF 2017. A 100-year review: identification and genetic selection of economically important traits in dairy cattle. Journal of Dairy Science 100, 10251–10271. doi:10.3168/jds.2017-12968 CrossRefGoogle ScholarPubMed
Miglior, F, Muir, BL and Van Doormaal, BJ 2005. Selection indices in Holstein cattle of various countries. Journal of Dairy Science 88, 1255–1263. doi:10.3168/jds.S0022-0302(05)72792-2 CrossRefGoogle ScholarPubMed
National Research Council (NRC) 2001. Nutrient requirements of dairy cattle, 7th revised edition. National Academic Press, Washington, DC, USA.Google Scholar
Opio, CP, Gerber, A, Mottet, A, Falcucci, G, Tempio, M, MacLeod, T, Vellinga, B, Henderson, B and Steinfeld, H 2013. Greenhouse gas emissions from ruminant supply chains – a global life cycle assessment. Food and Agriculture Organization of the United Nations, Rome, Italy.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, 7340–7350. doi:10.3168/jds.2015-9621 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, 1–10. doi: 10.1017/S1751731113001687 CrossRefGoogle ScholarPubMed
University of Alberta 2016. Efficient dairy genome project. Retrieved on 1 March 2017 from https://genomedairy.ualberta.ca Google Scholar
United States Board of Environmental Change and Society 2017. Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide. Retrieved on 10 July 2016 from http://nas-sites.org/americasclimatechoices/other-reports-on-climate-change/valuing-climate-damages-updating-estimation-of-the-social-cost-of-carbon-dioxide-2017/ 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. doi:10.3168/jds.S0022-0302(98)75673-5 CrossRefGoogle ScholarPubMed
Veerkamp, RF, Pryce, JE, Spurlock, D, Berry, DP, Coffey, MP, Løvendahl, P, van der Linde, R, Bryant, J, Miglior, F, Wang, Z, Winters, M, Krattenmacher, N, Charfeddine, N, Pedersen, J and de Haas, Y 2013. Selection on feed intake or feed efficiency: a position paper from gDMI breeding goal discussions. In Proceedings of the 47th Interbull Annual Meeting, 23–25 August 2013, Nantes, France, pp. 15–22.Google Scholar
Waghorn, GC and Hegarty, RS 2011. Lowering ruminant methane emissions through improved feed conversion efficiency. Animal Feed Science and Technology 166, 291–301. doi: 10.1016/j.anifeedsci.2011.04.019 CrossRefGoogle Scholar
Zwald, A, Kohlman, TL, Gunderson, SL, Hoffman, PC and Kriegl, T 2007. Economic costs and labor efficiencies associated with raising dairy herd replacements on Wisconsin dairy farms and custom heifer raising operations. University of Wisconsin Extension Publication, Madison, WI, USA.Google Scholar
Supplementary material: File

Richardson et al. supplementary material

Richardson et al. supplementary material 1

Download Richardson et al. supplementary material(File)
File 29.5 KB