Chagunda, M.G.

Garnsworthy, P.C.

Haas, Y., de

Lassen, J.

Lovendahl, P.

Negussie, E.

de Haas, Y.

NARCIS 

Progress with genetic selection for low methane traits in dairy cows

Garnsworthy, P. C.

Chagunda, M. G. G.

Negussie, Enyew

Løvendahl, P.

Jukuri

Name not available

Wageningen Yield 

Proceedings, 10th World Congress of Genetics Applied to Livestock Production 
 
Progress with genetic selection for low methane traits in dairy cows 
J. Lassen1, P. C. Garnsworthy2, M. G. G. Chagunda3, E. Negussie4, P. Løvendahl1 and Y. de Haas5 
1Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University,  
Denmark 2University of Nottingham School of Biosciences, Sutton Bonington Campus, Loughborough LE12 5RD, UK 
3Scotland’s Rural College (SRUC), Kings Buildings, Edinburgh,  UK 4MTT Agrifood Research, Jokioinen, Finland 5Animal 
Breeding and Genomics Centre,  Wageningen UR Livestock Research, Wageningen, The Netherlands 
 
ABSTRACT: Over the last five years a number of methods 
have been developed and adapted to measure and quantify 
methane emission from individual animals. Their 
approaches show similarities and yet also differences. In 
general, they focus on being noninvasive measuring in the 
cow’s everyday environment. In order to make genetic 
selection for methane emission it will be necessary to make 
large scale measurements in commercial farms. 
International collaboration, comparison and validation are 
essential to make progress in this area. It will be necessary 
to have strategic collection and exchange of data. More 
work is needed on how to get small-scale phenotypes in to 
large-scale genomic selection. 
Keywords: dairy cattle; methane; genetics; sustainability 
 
Introduction 
 
Climate change is a growing international concern 
and it is well established that release of greenhouse gases 
(GHG) is a contributing factor (Gerber et al. (2010)). Of the 
various GHG produced by ruminants, enteric methane 
(CH4) is the most important contributor, with a global 
warming potential 25 times that of carbon dioxide (CO2). 
Today CH4 emission as such does not have a direct 
economic value for the farmer but this may change if a tax 
is put on CH4 emission or through the relation to feed 
efficiency since approximately 6% of gross energy in feed 
the cow eats is eructed as CH4 (Johnson et al., (1993)). 
Therefore, a reduction in CH4 emission will not just be 
beneficial for the environment; it will also mean more 
efficient animals for the farmer. 
Our ability to reduce the carbon foot print of animal 
production through genetic strategies depends on the size of 
available genetic variation as well as accurate techniques 
needed for the measurement of phenotypes on a large scale.  
This means that starting selection for a new trait requires 
phenotypes measured in commercial farms. In order to get 
access to dairy cattle data from commercial farms it is 
important to develop methods that are non-invasive, require 
no training, and doesn’t interfere with the normal dairy 
routines of commercial farms. The equipment should also 
be portable, low cost and preferably with limited 
maintenance and a high capacity.  
With the recent successful incorporation of genomic 
information into breeding schemes the reliance on very 
large populations of phenotyped animals is somewhat 
relaxed. Nonetheless, a reference population of several 
thousand animals is still required to estimate the 
contribution of each genomic region to expression of the 
phenotype under investigation (Goddard and Hayes, 
(2009)). Therefore, a dataset of several thousand animals 
with similarly defined records for CH4 emissions would be 
sufficient for genetic evaluation of enteric CH4 emissions. 
In this study we describe a number of methods to measure 
CH4 from ruminants and the results in relation to potential 
to select for reduced CH4 emission from dairy cattle. 
 
Materials and Methods 
 
A number of approaches to measure CH4 from the 
breath samples of the cow have been developed.  
 
Denmark. In Denmark a method that measures gases 
during milking in milking robots has been used (Lassen et 
al., (2012)). A total of 2104 cows have been phenotyped in 
commercial farms and the focus is on using the ratio 
between CH4 and carbon dioxide (CO2) concentrations in 
the breath as the phenotype to correct for the placement of 
the cow’s head in relation to the measuring device.  
Another method, also measuring during robotic milking 
sessions is used at the Danish Cattle Research Centre. The 
method relies on infra-red detection with two commercially 
available instruments working in series (Guardian NG / 
Gascard; Edinburgh Instruments, UK) with data recording 
and logging every second. This equipment is the same as 
that used by Garnsworthy et al. (2012a). Readings are 
synchronized with robotic milking data for animal ID and 
links to other data. The research herd includes 120 
Holsteins plus 60 Jerseys, all recorded for feed intake, 
production and live weight. 
 
 The United Kingdom. In the UK a method has been 
developed to measure CH4 during milking in milking 
robots, which has been validated against measurements of 
CH4 in respiration chambers (Garnsworthy et al. (2012a)). 
A total of 2659 cows have been phenotyped on commercial 
farms and the focus is on using CH4 released by eructation 
as the phenotype to improve precision. A group of 215 
cows was monitored continuously for 5 months to 
investigate within-cow variation (Garnsworthy et al. 
(2012b)). 
In another UK study, the use of a proprietary laser 
CH4 detector (LMD) in livestock is being investigated. This 
was first suggested by Chagunda et al. (2009) and its 
application has since been explored (Chagunda and Yan 
(2011); Ricci et al. (2012)). The LMD is a hand-held gas 
detector for remote measurement of column density for 
methane-containing gases. Methane measurements by the 
LMD are based on infrared absorption spectroscopy, using 
a semiconductor laser as a collimated excitation source and 
employing the second harmonic detection of wavelength 
modulation spectroscopy to establish a CH4 concentration 
measurement (Iseki and Miyaji (2003)).  Concentration of 
CH4 is expressed in parts per million-metre (ppm-m), as the 
amount itself relates to both the concentration and the depth 
of CH4 respiratory plume. 
 
Finland. In Finland, the CH4 output of first-lactation 
cows from the MTT Agrifood Research experimental dairy 
farm has been continuously monitored using a F10 multi-
gas analyzer (GASERA Ltd. Turku, Finland) that is based 
on Photoacoustic Infrared Spectroscopy technique 
(Negussie et al. (2013)). This method is used for the 
measurement of individual cow CH4, CO2 and acetone 
outputs from the breath sample of cows via sampling tubes 
of the analyzer that are fitted to separate individual feeding 
kiosks. Simultaneously, along with the measurements of the 
gases, daily feed intake, production and functional traits are 
also recorded. From the measured gasses, for each cow 
CH4:CO2 was calculated and based on ME intake, daily 
CH4 outputs are estimated as shown in (Madsen et al. 
(2010)). Currently the database contains entire first-
lactation records of more than 90 cows with both daily feed 
intake, production as well as daily CH4 outputs (Table 1). 
 
The Netherlands. The same equipment and 
measurement strategy as in Denmark has been used in the 
Netherlands. Breath air was sampled and analyzed directly 
for CH4 and CO2 using a portable Fourier Transformed 
Infrared (FTIR) gas analyzer with 2 air inlets (GASMET 
4030; Gasmet Technologies Oy, Helsinki, Finland). One 
instrument air inlet was placed in front of the cow’s head in 
the milking robot, and one air inlet was placed at 2.5 meters 
height near the milking robot to measure background 
concentrations. Measurements were alternatively taken 
from one of the 2 air inlets; each measurement involved 
continuous sampling for 1 minute. Cows visited the milking 
robot between 1 and 4 times per day, and between 3 to 8 
measurements of 1 minute were recorded per visit. To date, 
400 genotyped cows have been phenotyped. Enteric CH4 
emissions measured from the breath during milking have 
been recorded for at least 6 weeks, and 100 of them have 
been continuously recorded for a full year. 
 
Results and Discussion 
 
Overall means and standard deviations for the 
different types of measuring units and approaches are 
presented in Table 1.  
In the Danish data a repeatability of 0.35 between 
measurements of the ratio between CH4 and CO2 has been 
found. Also a heritability of 0.21±0.06 of the same 
phenotype has been reported using data from milking robots 
(Lassen and Løvendahl (2013)). A heritability of that 
magnitude opens up for selection based on direct 
measurements 
In the UK data the repeatability of CH4 measurements 
online during milking ranged from 0.55 in cows measured 
at an interval of 5 months to 0.89 in cows measured at an 
interval of 1 week (Garnsworthy et al. (2012b). Methane 
emission rate recorded during milking on farm showed a 
linear relationship (R2 = 0.79) with daily CH4 output by the 
same cows when housed subsequently in respiration 
chambers (Garnsworthy et al. (2012a)). 
Table 1. Country, number of records, phenotype used, 
phenotypic means and standard deviations, 
repeatability and heritability in different European 
studies of methane (CH4) emission from dairy cattle 
CO # PH MEAN SD t h2 
DK 2104 CH4/CO2 0.089 0.03 0.35 0.21 
FIN 90      
  CH4g/day  330 58 0.27-
0.65 
 
NL 400 CH4 ppm 386 99   
UK 2659 CH4 g/d 418 349 0.55-
0.89 
 
UK 180 CH4 ppm 396 183 0.48  
CO = country 
# Number of animals with registrations 
PH = phenotype registered 
MEAN =phenotypic mean 
SD = Phenotypc standard deviation 
t = Repeatability 
h2 = Heritability 
DK = Denmark 
FIN = Finland 
UK = United Kingdom 
NL = The Netherlands 
 
Using the LMD To date 180 cows from the long-
running genetic x management systems study based at 
Scotland’s Rural College (SRUC) Dairy Research Centre, 
Crichton Royal Farm, UK have been phenotyped. After 
adjusting for plume effect, in dairy cows the mean breath 
CH4 has been found to be 395.8 sd 182.7 ppm while that for 
sheep was 68.9 sd 32.6 ppm (Chagunda et al. (2013)). In 
dairy cows, repeatability of the measurements 0.48 has been 
found (Chatepa (2012)). 
In the Finnish data, CH4 outputs of first-lactation 
cows were monitored for the entire lactation. Across 
lactation analysis of this data showed that the repeatability 
of CH4 output (gram/day) ranged from 0.27 to 0.65 for 
different stages of lactation (Negussie et al. (2014)). The 
result indicates that there is potential between-animal 
variation for the trait which could possibly be used for 
developing genetic tools to lower CH4 output from 
ruminant production systems.   
In the Dutch data, mean CH4 concentration was 386 
ppm, with a range of individual cow means between 196 
and 724 ppm, and a standard deviation of 99 ppm. Mean 
CO2 concentration was 6380 ppm, with a range between 
4280 and 14,500 ppm, and a standard deviation of 1900 
ppm. Daily means of the background concentrations were 
35 ppm for CH4 and 591 ppm for CO2. The high 
concentrations measured in the milking robot indicate that 
the sampled air included a high portion of exhaled air of the 
milked cow. 
Direct measurements might not be the only 
opportunity to select for lower CH4 emissions from dairy 
cattle. Studies have shown promising results in using milk 
spectral data to predict CH4 emission (Dehareng et al. 
(2012)) or feed intake data (de Haas et al. (2011)). The 
dairy cattle sector is very experienced in handling milk 
analysis and the farmers are used to having milk samples 
recorded on farm so if the use of milk spectral data can be 
used it would be a much more efficient and powerful way 
to select for reduced CH4 emission from dairy cattle. In 
order to justify selection for less emitting dairy cattle an 
economic value for the trait has to be estimated. This could 
come from a tax on CH4 emission or from establishing the 
genetic relation to feed intake and/or feed efficiency. 
In all data that is presented in this study cows were 
feed ad libitum, and no restrictions were made that could 
affect the repeatability. Had restrictions been made daily 
variation could be relatively controlled and that would 
affect repeatability. Feeding ad libitum is more and more 
the feeding regime in dairy cattle production in Europe.  
A number of international initiatives have been 
established to help the process of validation, exchange of 
data and experiences and to make consensus on which 
phenotypes should be reported for CH4 production from 
dairy cattle. This is mainly important to be able to combine 
CH4 measurements performed with different equipment and 
protocols. Even though a number of countries have started 
to record individual CH4 data, none of them has enough 
records available to get accurate breeding values for this 
trait to be used in their national breeding program. The only 
way to use selective breeding as a mitigation strategy that is 
permanent and cumulative, is to create an international 
database with individual measurements. Therefore, a work 
group has been initiated under ICAR, and the ASGGN 
network has also been running and worked on a white paper 
for CH4 phenotypes, and in December 2013 the EU-COST-
Action METHAGENE was established to work on aspects 
of selection for reduced CH4 emission from dairy cattle. 
  
Conclusion 
 
A number of approaches are currently being tested 
and evaluated to measure CH4 from dairy cattle. In general 
they show promising results concerning repeatability 
between measurements. An upcoming task is to compare 
these methods with each other in order to make 
international collaboration and to be able to merge data 
from different devices and environments.   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Literature Cited 
 
Chagunda, M. G. G., D. Ross, and D. J. Roberts. (2009) 
Comp. Electr. Agric. 68:157-160. 
Chagunda, M. G. G. & Yan, T. (2011) Anim. Feed Sci. 
Techn. 165, 8-14. 
Chagunda, M.G.G., Ross, D., Rooke, J., et al. (2013)  Acta 
Agr. Scand., Section A – Anim. Sci. 63 (2): 68-75. 
Chatepa, L.E.C. (2012) Mphil Thesis, University of 
Glasgow. 
Dehareng F., Delfosse, C., Frodimont, E. et al. (2012) 
Anim. 10:1694-1701. 
De Haas Y, Windig JJ, Calus MPL, et al. (2011) J Dairy Sci 
94:6122-34. 
Garnsworthy, P. C., J. Craigon, J. H. Hernandez-Medrano 
et al. 2012 a. J. Dairy Sci. 95:3166–3180. 
Garnsworthy, P. C., J. Craigon, J. H. Hernandez-Medrano 
et al. 2012 b. J. Dairy Sci. 95:3181–3189. 
Gerber, P., Key, N., Portet, F. et al (2010) Anim. 4:393-
406. 
Goddard M. E. and B. Hayes 2009 Nat Rev Gen 10:381-
391. 
Iseki, T. & Miyaji, M. (2003). Proceedings, 22nd World 
Gas Congress, June 1-5, Tokyo, Japan. 
Johnson DE, TM Hill, GM Ward, et al (1993) Global Env. 
Change, 13:219-229 . 
Lassen, J., Løvendahl, P., J. Madsen (2012) J. Dairy Sci. 
95:890-898. 
Lassen, J. and Løvendahl, P. 2013. Adv in Anim Biosci 
4:280. 
Negussie, E., Mäntysaari, P., Mantysaari, E, et al. 2013. 
Adv in Anim Biosci 4:463. 
Negussie, E., Mäntysaari, P., Mantysaari, E, et al. 2014. 
Proceedings of the 10th WCGALP, Canada. 
Ricci, P., Chagunda, M. G. G., Duthie, et al. (2012). 
Proceedings of the 2nd Joint Conference of the New 
Zealand and Australian Societies of Animal Production, 
July 2-5, Lincoln University, Lincoln. 


Progress with genetic selection for low methane traits in dairy cows

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