Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-28T02:54:33.306Z Has data issue: false hasContentIssue false

A methodology framework for weighting genetic traits that impact greenhouse gas emission intensities in selection indexes

Published online by Cambridge University Press:  11 July 2017

P. R. Amer*
Affiliation:
AbacusBio Limited, PO Box 5585, Dunedin, New Zealand
F. S. Hely
Affiliation:
AbacusBio Limited, PO Box 5585, Dunedin, New Zealand
C. D. Quinton
Affiliation:
AbacusBio Limited, PO Box 5585, Dunedin, New Zealand
A. R. Cromie
Affiliation:
Irish Cattle Breeding Federation, Highfield House, Shinagh, Bandon, County Cork, Ireland
*
Get access

Abstract

A methodological framework was presented for deriving weightings to be applied in selection indexes to account for the impact genetic change in traits will have on greenhouse gas emissions intensities (EIs). Although the emission component of the breeding goal was defined as the ratio of total emissions relative to a weighted combination of farm outputs, the resulting trait-weighting factors can be applied as linear weightings in a way that augments any existing breeding objective before consideration of EI. Calculus was used to define the parameters and assumptions required to link each trait change to the expected changes in EI for an animal production system. Four key components were identified. The potential impact of the trait on relative numbers of emitting animals per breeding female first has a direct effect on emission output but, second, also has a dilution effect from the extra output associated with the extra animals. Third, each genetic trait can potentially change the amount of emissions generated per animal and, finally, the potential impact of the trait on product output is accounted for. Emission intensity weightings derived from this equation require further modifications to integrate them into an existing breeding objective. These include accounting for different timing and frequency of trait expressions as well as a weighting factor to determine the degree of selection emphasis that is diverted away from improving farm profitability in order to achieve gains in EI. The methodology was demonstrated using a simple application to dairy cattle breeding in Ireland to quantify gains in EI reduction from existing genetic trends in milk production as well as in fertility and survival traits. Most gains were identified as coming through the dilution effect of genetic increases in milk protein per cow, although gains from genetic improvements in survival by reducing emissions from herd replacements were also significant. Emission intensities in the Irish dairy industry were estimated to be reduced by ~5% in the last 10 years because of genetic trends in production, fertility and survival traits, and a further 15% reduction was projected over the next 15 years because of an observed acceleration of genetic trends.

Type
Research Article
Copyright
© The Animal Consortium 2017 

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

Archer, JA, Richardson, EC, Herd, RM and Arthur, PG 1999. Potential for selection to improve efficiency of feed use in beef cattle: a review. Australian Journal of Agricultural Research 50, 147151.Google Scholar
Bell, MJ, Eckard, RJ, Haile-Mariam, M and Pryce, JE 2013. The effect of changing cow production and fitness traits on net income and greenhouse gas emissions from Australian dairy systems. Journal of Dairy Science 96, 79187931.Google Scholar
Blaxter, KL and Clapperton, JL 1965. Prediction of the amount of methane produced by ruminants. British Journal of Nutrition 19, 511522.Google Scholar
Buckley, F, O’Sullivan, K, Mee, JF, Evans, RD and Dillon, P 2003. Relationships among milk yield, body condition, cow weight, and reproduction in spring-calved Holstein-Friesians. Journal of Dairy Science 86, 23082319.Google Scholar
Cottle, DJ, Nolan, JV and Wiedemann, SG 2011. Ruminant enteric methane mitigation: a review. Animal Production Science 51, 491514.Google Scholar
de Haas, Y, Davis, S, Reisinger, A, Richards, MB, Difford, G and Lassen, J 2016. Improved ruminant genetics: implementation guidance for policymakers and investors. Retrieved on 17 January 2017 from https://ccafs.cgiar.org/publications/improved-ruminant-genetics-implementation-guidance-policymakers-and-investors#.WH6IM_l96Ul Google Scholar
Gerber, PJ, Steinfeld, H, Henderson, B, Mottet, A, Opio, C, Dijkman, J, Falcucci, A and Tempio, G 2013. Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy.Google Scholar
Herrero, M, Grace, D, Njuki, J, Johnson, N, Enahoro, D, Silvestri, S and Rufino, MC 2013. The role of livestock in developing countries. Animal 7 (suppl. 1), 318.Google Scholar
James, JW 1981. Index breeding for simultaneous improvement of several characters. In Proceedings of the 14th International Congress on Genetics (ed. D Belyaer), pp. 221228. MIR Moscow, USSR.Google Scholar
Ludemann, C, Byrne, T, Sise, JA and Amer, PR 2011. Potential for New Zealand farmers to reduce sheep greenhouse gas emissions through genetic selection tools. Proceedings of the 18th International Farm Management Congress, 20 to 25 March 2011, Methven, New Zealand, pp. 18–26.Google Scholar
National Research Council (NRC) 2001. Nutrient requirements of dairy cattle. National Academy Press, Washington, DC, USA.Google Scholar
Nielsen, HM, Amer, PR and Byrne, TJ 2014. Approaches to formulating practical breeding objectives for animal production systems. Acta Agriculturae Scandinavica 64, 212.Google Scholar
O’Brien, D, Brennan, P, Humphreys, J, Ruane, E and Shalloo, L 2014. An appraisal of carbon footprint of milk from commercial grass-based dairy farms in Ireland according to a certified life cycle assessment methodology. International Journal of Life Cycle Assessment 19, 14691481.Google Scholar
O’Brien, D, Hennessy, T, Moran, B and Shalloo, L 2015. Relating the carbon footprint of milk from Irish dairy farms to economic performance. Journal of Dairy Science 98, 73947407.Google Scholar
O’Brien, D, Shalloo, L, Grainger, C, Buckley, F, Horan, B and Wallace, M 2010. The influence of strain of Holstein-Friesian cow and feeding system on greenhouse gas emissions from pastoral dairy farms. Journal of Dairy Science 93, 33903402.Google Scholar
O’Mara, FP 2006. Development of emission factors for the Irish cattle herd (2000-LS-5.1.1-M1): special report (ERTDI Report 46). Environmental Protection Agency, Wexford, Ireland.Google Scholar
Pickering, NK, Chagunda, MGG, Banos, G, Mrode, R, McEwan, JC and Wall, E 2015. Genetic parameters for predicted methane production and laser methane detector measurements. Journal of Animal Science 92, 1120.CrossRefGoogle Scholar
Wall, E, Ludemann, CI, Jones, H, Audsley, E, Moran, D, Roughsedge, T and Amer, PR 2010a. The potential for reducing greenhouse gas emissions for sheep and cattle in the UK using genetic selection. Science Advisory Council, Department of Environment, Food and Rural Affairs, UK. Retrieved on 4 July 2017 from http://randd.defra.gov.uk/Document.aspx?Document=FinalReportIF0182.doc.Google Scholar
Wall, E, Simm, G and Moran, D 2010b. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal 4, 366376.Google Scholar
Watson, RT, Zinyowera, MC and Moss, RH 1996. Technologies, policies and measures for mitigating climate change: technical paper for the Intergovernmental Panel on Climate Change (IPCC) working group 2. Retrieved on 17 January 2017 from https://www.ipcc.ch/pdf/technical-papers/paper-I-en.pdf Google Scholar
Supplementary material: File

Amer supplementary material

Amer supplementary material 1

Download Amer supplementary material(File)
File 44.1 KB