Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-18T05:50:27.676Z Has data issue: false hasContentIssue false

Is there a relationship between genetic merit and enteric methane emission rate of lactating Holstein-Friesian dairy cows?

Published online by Cambridge University Press:  12 August 2015

L. F. Dong
Affiliation:
Agri-Food and Biosciences Institute, Hillsborough, Co. Down BT26 6DR, UK Faculty of Life and Health Sciences, University of Ulster, Newtownabbey, Co. Antrim BT37 0QB, UK
T. Yan*
Affiliation:
Agri-Food and Biosciences Institute, Hillsborough, Co. Down BT26 6DR, UK
C. P. Ferris
Affiliation:
Agri-Food and Biosciences Institute, Hillsborough, Co. Down BT26 6DR, UK
D. A. McDowell
Affiliation:
Faculty of Life and Health Sciences, University of Ulster, Newtownabbey, Co. Antrim BT37 0QB, UK
A. Gordon
Affiliation:
Agri-Food and Biosciences Institute, Newforge, Co. Down BT9 5PX, UK
Get access

Abstract

The present study was undertaken to examine the effect of cow genetic merit on enteric methane (CH4) emission rate. The study used a data set from 32 respiration calorimeter studies undertaken at this Institute between 1992 and 2010, with all studies involving lactating Holstein-Friesian dairy cows. Cow genetic merit was defined as either profit index (PIN) or profitable lifetime index (PLI), with these two United Kingdom genetic indexes expressing the expected improvement in profit associated with an individual cow, compared with the population average. While PIN is based solely on milk production, PLI includes milk production and a number of other functional traits including health, fertility and longevity. The data set had a large range in PIN (n=736 records, −£30 to +£63) and PLI (n=548 records, −£131 to +£184), days in milk (18 to 354), energy corrected milk yield (16.0 to 45.6 kg/day) and CH4 emission (138 to 598 g/day). The effect of cow genetic merit (PIN or PLI) was evaluated using ANOVA and linear mixed modelling techniques after removing the effects of a number of animal and diet factors. The ANOVA was undertaken by dividing each data set of PIN and PLI into three sub-groups (PIN:<£3, £3 to £15 and >£15, PLI:<£23, £23 to £67 and >£67) with these being categorised as low, medium and high genetic merit. Within the PIN and PLI data sets there was no significant differences among the three sub-groups in terms of CH4 emission per kg feed intake or per kg energy corrected milk yield, or CH4 energy (CH4-E) output as a proportion of energy intake. Linear regression using the whole PIN and PLI data sets also demonstrated that there was no significant relationship between either PIN or PLI, and CH4 emission per kg of feed intake or CH4-E output as a proportion of energy intake. These results indicate that cow genetic merit (PIN or PLI) has little effect on enteric CH4 emissions as a proportion of feed intake. Instead enteric CH4 production may mainly relate to total feed intake and dietary nutrient composition.

Type
Research Article
Copyright
© The Animal Consortium 2015 

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

Bell, MJ, Potterton, SL, Craigon, J, Saunders, N, Wilcox, RH, Hunter, M, Goodman, JR and Garnsworthy, PC 2014. Variation in enteric methane emissions among cows on commercial dairy farms. Animal 8, 15401546.CrossRefGoogle ScholarPubMed
Bell, MJ, Wall, E, Russell, G and Simm, G 2010. Effect of breeding for milk yield, diet and management on enteric methane emissions from dairy cows. Animal Production Science 50, 817826.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 166–167, 699707.Google Scholar
Chagunda, MGG, Römer, DAM and Roberts, DJ 2009. Effect of genotype and feeding regime on enteric methane, non-milk nitrogen and performance of dairy cows during the winter feeding period. Livestock Science 122, 323332.Google Scholar
DairyCo Breeding 2013. Holstein cow breed standards. Retrieved January, 7 2013, from http://www.dairycobreeding.org.uk/tables.asp?b=HOL&t=SAC_cow_report_breedstandards_hol.Google Scholar
Ellis, JL, Dijkstra, J, France, J, Parsons, AJ, Edwards, GR, Rasmussen, S, Kebreab, E and Banninkll, A 2012. Effect of high-sugar grasses on methane emissions simulated using a dynamic model. Journal of Dairy Science 95, 272285.Google Scholar
Ferris, CP, Gordon, FJ, Patterson, DC, Mayne, CS and Kilpatrick, DJ 1999. The influence of dairy cow genetic merit on the direct and residual response to level of concentrate supplementation. Journal of Agricultural Science 132, 467481.Google Scholar
Fitzsimons, C, Kenny, DA, Deighton, MH, Fahey, AG and McGee, M 2013. Methane emissions, body composition, and rumen fermentation traits of beef heifers differing in residual feed intake. Journal of Animal Science 91, 57895800.Google Scholar
Garnsworthy, PC, Craigon, J, Hernandez-Medrano, JH and Saunders, N 2012. Variation among individual dairy cows in methane measurements made on farm during milking. Journal of Dairy Science 95, 31813189.Google Scholar
Gordon, FJ, Porter, MG, Mayne, CS, Unsworth, EF and Kilpatrick, DJ 1995. The effect of forage digestibility and type of concentrate on nutrient utilisation for lactating dairy cattle. Journal of Dairy Research 62, 1527.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, 14791486.Google Scholar
Herd, RM, Arthur, PF, Hegarty, RS and Archer, JA 2002. Potential to reduce greenhouse gas emissions from beef production by selection for reduced residual feed intake. In Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, pp. 0–4.Google Scholar
Holstein UK 2013. Education – a guide to genetic indexes 2013. Retrieved January 7, 2013, from http://ukcows.com/holsteinUK/publicweb/Education/HUK_Edu_GenIndex.aspx?cmh=66.Google Scholar
Hristov, AN, Oh, J, Firkins, JL, Dijkstra, J, Kebreab, E, Waghorn, G, Makkar, HPS, Adesogan, AT, Yang, W, Lee, C, Gerber, PJ, Henderson, B and Tricarico, JM 2013. Special topics – Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. Journal of Animal Science 91, 50455069.Google Scholar
Hulshof, RBA, Berndt, A, Gerrits, WJJ, Dijkstra, J, van Zijderveld, SM, Newbold, JR and Perdok, HB 2012. Dietary nitrate supplementation reduces methane emission in beef cattle fed sugarcane based diets. Journal of Animal Science 90, 23172323.Google 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 166–167, 302307.Google Scholar
Mills, JAN, Kebreab, E, Yates, CM, Crompton, LA, Cammell, SB, Dhanoa, MS, Agnew, RE and France, J 2003. Alternative approaches to predicting methane emissions from dairy cows. Journal of Animal Science 81, 31413150.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 behaviour with metabolic rate, methane production, and energy partitioning in beef cattle. Journal of Animal Science 84, 145153.CrossRefGoogle ScholarPubMed
Opio, C, Gerber, P, Mottet, A, Falcucci, A, Tempio, G, MacLeod, M, Vellinga, T, 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 (FAO), Rome.Google Scholar
Payne, RW, Harding, SA, Murray, DA, Soutar, DM, Baird, DB, Glaser, AI, Channing, IC, Welham, SJ, Gilmour, AR, Thompson, R and Webster, R 2013. The guide to GenStat release 16, part 2: statistics. VSN International, Hemel Hempstead.Google Scholar
Ramin, M and Huhtanen, P 2013. Development of equations for predicting methane emissions from ruminants. Journal of Dairy Science 96, 24762493.Google Scholar
Tyrrell, HF and Reid, JT 1965. Prediction of the energy value of cow’s milk. Journal of Dairy Science 48, 12151223.Google Scholar
Veerkamp, RF and Emmans, GC 1995. Sources of genetic variation in energetic efficiency of dairy cows. Livestock Production Science 44, 8797.Google Scholar
Yan, T, Agnew, RE, Gordon, FJ and Porter, MG 2000. The prediction of methane energy output in dairy and beef cattle offered grass silage-based diet. Livestock Production Science 64, 253263.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, 26302638.Google Scholar
Yan, T, Mayne, CS, Keady, TW and Agnew, RE 2006. Effects of dairy cow genotype with two planes of nutrition on energy partitioning between milk and body tissue. Journal of Dairy Science 89, 10311042.Google Scholar
Zhou, M, Hernandez-Sanabria, E and Guan, LL 2009. Assessment of the microbial ecology of ruminal methanogens in cattle with different feed efficiencies. Applied and Environmental Microbiology 45, 65246533.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, 37763786.Google Scholar
Supplementary material: File

Dong supplementary material

Dong supplementary material S1

Download Dong supplementary material(File)
File 43.1 KB