Section 20 – Methane Emission for Genetic Evaluation

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Introduction

Increases in milk production through management and genetics have substantially improved feed efficiency and decreased costs per unit of product over recent decades. However, dairy systems are also associated with environmental costs (Baskaran et al., 2009[1]), with methane (CH4) emissions associated with rumen microbial fermentation being both an important contributor to global greenhouse gas (GHG) emissions, as well as an avoidable loss of energy that could otherwise be directed into milk production. The livestock sector is responsible for 14.5% of the global GHG (Gerber et al., 2013[2]); dairy cattle account for 18.9% of these emissions, mainly in the form of enteric CH4 emissions (van Middelaar et al., 2014[3]). Methane is a greenhouse gas with a global warming potential 28 times that of CO2 (Myhre et al., 2013[4]). Methane from ruminant livestock is generated during microbial fermentation in the rumen and hindgut (enteric CH4), and from decomposition of manure. Enteric CH4 contributes 80% of CH4 emissions by ruminants, and manure decomposition contributes 20%. Enteric CH4 accounts for 17% of global CH4 emissions and 3.3% of total global greenhouse gas emissions from human activities (Knapp et al., 2014[5]). There is, therefore, a significant research interest to find ways to reduce enteric CH4 emissions by ruminants. Ruminant animals have a digestive system to digest plant materials efficiently. Like most mammals, ruminants lack the cellulase enzyme required to break the beta-glucose linkages in cellulose, but they play host to diverse populations of rumen microbes that can digest cellulose and other plant constituents. When rumen bacteria, protozoa and fungi ferment carbohydrates and proteins of plant materials, they produce volatile fatty acids, principally acetate, propionate and butyrate. High fibre diets favour acetate synthesis. Synthesis of acetate and butyrate are accompanied by release of metabolic hydrogen, which, if allowed to accumulate in rumen fluid, has negative effects on microbial growth, and feed digestibility (Janssen, 2010[6]). Rumen Archaea are microorganisms that combine metabolic hydrogen with CO2 to produce CH4 and water. Archaea play a vital role, therefore, in protecting the rumen from excess metabolic hydrogen, and the CH4 they produce is an inevitable product of rumen fermentation. A number of CH4 phenotypes have been defined (Hellwing et al., 2012[7]); the most widely used is CH4 production (MeP) in liters or grams per day. The CH4 production trait is highly correlated with feed intake (Basarab et al., 2013[8]; De Haas et al., 2017[9]) and, thereby, with the ultimate breeding goal trait: milk production in dairy cattle. The economic value of daily dry matter intake and associated methane emissions in dairy cattle showed that increasing the feed performance 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 (Richardson et al., 2019[10]). Feed Performance was defined as a 1 kg increase in more efficiently used feed in a first parity lactating cow. These results show not only the relation between DMI and CH4 production, but also the economic relationship between these traits. Persistency of lactation was found to be positively associated with increased feed efficiency and decreased methane production and intensity. Feed efficiency was associated with lower methane intensity. Feed efficiency and methane emissions can be improved by selecting for dairy cattle that are smaller and have increased persistency of lactation. Efficiency and methane emissions can be further improved by improved management of body condition score and by extending lactations beyond the conventional 305-day length (Seymour, 2019[11]). According to Ellis et al. (2007)[12], DMI predicted MeP with an R2 of 0.64, and ME intake (MJ/d) predicted MeP with an R2 of 0.53 for dairy cattle. AlternativePhenotype definitions include CH4 intensity (MeI), which is defined as liters or grams of CH4 per kg of milk, and CH4 yield (MeY), which is defined as liters or grams of CH4 per kg of dry matter intake (DMI) (Moate et al., 2016[13]). Residual CH4 production (RMP) is calculated as observed minus predicted CH4 production (Herd et al., 2014[14], Berry et al., 2015[15]), with predicted values based on factors such as milk production, body weight and feed intake. At the moment, it is not obvious which of these phenotypes to use; but, it is important to monitor associations between the chosen CH4 phenotype and the other important traits in the breeding goal (e.g. production, fertility, longevity) to avoid unfavorable consequences. Berry and Crowley (2012)[16] describe advantages and limitations of ration traits. For example, because feed efficiency traits are a linear combination of other traits it is not recommended to include them in an overall total merit index, which is a clear limitation. For all applications it is necessary to measure the CH4 emission of each animal individually. These guidelines are intended to make the right choices for this. Whilst diet changes and feed additives can be effective mitigation strategies for CH4 emissions (Beauchemin et al., 2009[17]; Martin et al., 2010[18]; Hristov et al., 2013[19]), their effects depend on the continued use of a particular diet or additive and there have been issues with the rumen microbiomes adapting to additives. Rumen bacterial communities are highly dynamic after a diet switch and did not stabilize within 5 wk of cows grazing pasture (Bainbridge et al., 2016[20]). In contrast, breeding for reduced CH4 emissions should result in a permanent and cumulative reduction of emissions (Wall et al., 2010[21]). Several studies have shown that CH4 emissions by ruminants have a genetic component, with heritability in the range 0.20 – 0.30 (de Haas et al., 2011[22]; Donoghue et al., 2013[23]; Pinares-Patiño et al., 2013[24], Kandel et al., 2014A[25], B[26]; Lassen and Lovendahl, 2016[27]; López-Paredes et al. 2020[28]). Breeding for reduced CH4 emissions, alone or together with other mitigation strategies, could therefore be effective in reducing the environmental impact of cattle farming and, possibly, also in increasing feed efficiency. Such a breeding scheme would require, as a fundamental starting point, accurate measures of individual CH4 emissions on a large scale. Several techniques have been developed for the measurement of CH4 emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013[29] and Hammond et al., 2016A[30]), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult to obtain and expensive to measure (Pickering et al., 2015[31]; Negussie et al., 2016[32]). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH4 emissions, but which are easy and relatively lowcost to record on a large scale, would be a welcome alternative. Proxies might be less accurate but could be measured repeatedly to reduce random noise and in much larger populations. These guidelines are highly indebted to Garnsworthy et al. (2019)[33]. In this paper the methods to measure CH4 are compared with special emphasis to the genetic evaluation of dairy cattle.

Disclaimer

The fact that specific device manufacturers are mentioned in these guidelines is in no way an endorsement of the devices or their accuracy by ICAR.

Scope

A variety of technologies are being developed and employed to measure CH4 emissions of individual dairy cattle under various environmental conditions, as is evidenced by frequent reviews (Storm et al., 2012[34]; Cassandro et al., 2013[35]; Hammond et al., 2016A[36]; de Haas et al., 2017[37]). The first objective of the current guidelines is to review and compare the suitability of methods for large-scale measurements of CH4 output of individual animals, which may be combined with other databases for genetic evaluations. Comparisons include assessing the accuracy, precision and correlation between methods. Combining datasets from different countries and research centres could be a successful strategy for making genetic progress in this difficult to measure trait if the methods are correlated (de Haas et al., 2017[9]). Accuracy and precision of methods are important. Data from different sources need to be appropriately weighted or adjusted when combined, so any methods can be combined if they are suitably correlated with the ‘true’ value. The second objective of the current guidelines, therefore, is to examine correlations among results obtained by different methods, ultimately leading to an estimate of confidence limits for selecting individual animals that are high or low emitters (see also Garnsworthy et al., 2019[33]).

Methane determining factors

Diet and rumen microbiota

Table 1 contains a list of dietary or microbiota factors that determine CH4 production.

Table 1. Methane determining factors related to diet and rumen microbiota.
Factors Reference
The main determinants of daily methane production are dry matter intake and diet composition: the more feed consumed, and/or the greater the fibre content of the diet, the more methane is produced per day. However, per unit of DMI, and per unit of fat+protein yield the grass diet produced less enteric CH4 per cow than the TMR diet. Nutritional approaches for methane mitigation include reducing the forage to concentrate ratio of diets, increasing dietary oil content, and dietary inclusion of rumen modifiers and methane inhibitors. Beauchemin et al., 2009[38];

Cottle et al., 2011[39]; Knapp et al., 2014[40]; O’Neill et al., 2011[41]; Sauvant et al., 2011[42]

Methane output per kg of product is affected mainly by cow milk yield or growth rate, and by herd-level factors, such as fertility, disease incidence and replacement rate. Garnsworthy, 2004[43]
Methane output varies considerably between individual animals. For animals fed the same feed, the between-animal coefficient of variation (CV) in methane was 8.1%. Blaxter and Clapperton, 1965[44]
The amount of digestible nutrients consumed especially of the carbohydrate fraction (starch, sugar, N-free residuals) is reliable to estimate CH4 release with high precision. Furthermore, diets rich in fat reduced CH4 formation in the rumen. Jentsch et al., 2007[45]
DMI was also the most important determining factor, but there were different regression lines for maize silage and dried grass as the main roughage component respectively:

Methane release was particularly dependent on the intake of crude fiber (CF) and ether extract (EE):

Kirchgessner et al., 1991[46]
Methane linearly increased with NDF intake for cows together with their calves independent of the breed. Estermann et al., 2002[47]
Enteric CH4 could be predicted with the equation:

Hindrichsen et al., 2005[48]
The higher the percentage concentrate the lower Ym. Zeitz et al., 2012[49]
Additives can sometimes have a methane reducing effect: higher dosages mitigate methane more. Saponins mitigate methanogenesis by reducing the number of protozoa, whereas condensed tannins act both by reducing the number of protozoa and by a direct toxic effect on methanogens. Beauchemin et al., 2008;[50]

Jayanegara et al.[51], 2012; Zmora et al., 2012[52]; Cieslak et al., 2013[53]; Guyader et al., 2014[54]

Plant essential oils have been shown as promising feed additives to mitigate CH4 and ammonia emission, but results were inconsistent. Cobellis et al., 2016;[55]

Moate et al., 2011[56]

Nitrate and sulphate addition decreased the enteric methane emissions negatively affecting diet digestibility and milk production. The effects of the salts are additive. van Zijderveld et al., 2010[57];

van Zijderveld et al., 2011[58]

The methanogenesis in the rumen of calves is associated with the development of the ruminal protozoa population. The absence of protozoa in the rumen reduced both the CH4 production and the digestibility of carbohydrates. Schönhusen et al., 2003[59]
Implementing good grazing management reduced gross energy intake loss as CH4 by 14%. Wims et al., 2010[60]

Host genetics, physiology and environment

A low-moderate proportion of variation in CH4 emissions among ruminants is under genetic control. Heritability coefficients of MeY and RMPR were h²=0.22 and 0.19 respectively in a population of 1,043 Angus growing steers and heifers measured during 2 days in RC (Donoghue et al., 2016[61]). The heritability coefficient of MeY was h²=0.13 in a population of1,225 dual-purpose growing sheep measured during 2 days in RC (Pinares-Patino et al., 2013[62]). Table 2 contains information of heritability of traits related to CH4 production.

Table 2. Heritability information of methane-related traits and measurements.
Factors Reference
List with several h2 Pickering et al., 2015[63]
List with several h2 MPWG White paper Dec 18[64]
Methane emissions from individual cows during milking varied between individuals with the same milk yield and fed the same diet. Between-cow variation in MERm is greater than within-cow variation and ranking of cows for CH4 emissions is consistent across time. Variation related to body weight, milk yield, parity, and week of lactation/days in milk. The monitored variation might offer opportunities for genetic selection. Garnsworthy et al., 2011A[65];

Garnsworthy et al., 2011B[66]

Mechanistic modelling approach: potential for dietary intervention as a means of substantially reducing CH4 emissions without adverse effects on dietary energy supply. Mills et al., 2001[67]
The CH4-to-CO2 ratio measured using the non-invasive portable air sampler and analyzer unit based on Fourier transform infrared (FTIR) detection method is an asset of the individual cow and may be useful in both management and genetic evaluations. Lassen et al., 2012[68]
The estimated heritability for CH4 g/day and CH4 g/kg of FPCM were lower than common production traits but would still be useful in breeding programs. Kandel et al., 2013[69]
Genetic correlation between CH4 intensity and milk yield (MY) was - 0.67 and with milk protein yield (PY) was -0.46 in Holstein cows. Kandel et al., 2014A, B[70]
Milk production and CH4 emissions of dairy cows seemed to be influenced by the temperature humidity index. Vanrobays et al., 2013A[71]
Estimate the heritability of the estimated methane emissions from 485 Polish Holstein-Friesian dairy cows at 2 commercial farms using FTIR spectroscopy during milking in an automated milking system by implementing the random regression method. The heritability level fluctuated over the course of lactation, starting at 0.23 (SE 0.12) and then increasing to its maximum value of 0.3 (SE 0.08) at 212 DIM and ending at the level of 0.27 ± 0.12. Average heritability was 0.27 ± 0.09. Pszczola et al., 2017[72]
CH4 measured with a portable air-sampler FTIR detection method on 3,121 Holstein dairy cows from 20 herds using automatic milking systems. The heritability of CH4_MILK was 0.21 ± 0.06. It was concluded that a high genetic potential for milk production will also mean a high genetic potential for CH4 production. The results suggested that CH4 emission is partly under genetic control, that it is possible to decrease CH4 emission from dairy cattle through selection, and that selection for higher milk yield will lead to higher genetic merit for CH4 emission/cow per day. Lassen and Løvendahl, 2016[73]
CH4 production was measured of 184 Holstein-Friesian cows in. the milking robot with a in total 2,456 observations for CH4 production. Heritability for CH4 production ranged from 0.12 ± 0.16 to 0.45 ± 0.11, and genetic correlations with MY ranged from 0.49 ± 0.12 to 0.54 ± 0.26. The positive genetic correlation between CH4 production and milk yield indicates that care needs to be taken when genetically selecting for lower CH4 production, to avoid a decrease in MY at the animal level. However, this study shows that CH4 production is moderately heritable and therefore progress through genetic selection is possible. Breider et al., 2019[74]
CH4 concentration was measured with NDIR, and CH4 production was estimated from CH4 concentration and body weight. Heritability for CH4 concentration was 0.11 ± 0.03 and for CH4 production 0.12 ± 0.04. Positive genetic correlation was observed with MY (0.17-0.21), PY (0.22-0.31) and FY (0.27-0.29). Other type traits showed positive correlation with methane production (chest width=0.26, angularity =0.19, stature = 0.43 and capacity = 0.31) possibly associated to higher milk feed intake from these animals. Rumination time was negatively correlated to CH4 production (-0.24) and CH4 concentration (-0.43). However, larger CH4 production and CH4 concentration was associated with shorter days open. López-Paredes et al. (2020)[75]
Genetic parameters of CH4 emissions predicted from milk fatty acid profile (FA) and those of their predictors in 1,091 Brown Swiss cows reared on 85 farms showed that enteric CH4 emissions of dairy cows can be estimated on the basis of milk fatty acid profile. Additive genetic variation of CH4 traits was shown which could be exploited in breeding programmes. Bittante and Cecchinato, 2020[76]
A total of 670 test day records were recorded on lactating Holstein Friesian cows reared in 10 commercial dairy herds. Predicted methane production (PMP) was estimated to be 15.33±1.52 MJ/d in dairy cows with 23.53±6.81 kg/d of milk yeild (MY) and 3.57±0.68% of fat content (FC). Heritability of MY was 0.09 with a posterior probability for values of h2 greater than 0.10 of 44%. Estimates of heritability for FC and protein content (PC) were 0.17 and 0.34, respectively, with a posterior probability for values of h2 greater than 0.10 of 77% and 99%. For somatic cell score (SCS), heritability was 0.13 with a posterior probability for values of h2 greater than 0.10 of 67%. Heritability for the trait PMP was moderate to low (0.12); however, posterior probability for values of h2 greater than 0.10 was 60%. Medians of the posterior distributions of genetic correlations between PMP and milk production traits were: 0.92, 0.67, 0.14, and 0.14 between PMP and MY, PMP and FC, PMP and PC, and PMP and SCS, respectively. Reduction of PMP seems to be viable through selection strategies without affecting udder health and PC. Cassandro et al., 2010[77]
GWAS to study the genetic architecture of CH4 production and detected genomic regions affecting CH4 production. Detected regions explained only a small proportion of the heritable variance. Potential QTL regions affecting CH4 production were located within QTLs related to feed efficiency, milk-related traits, body size and health status. Five candidate genes were found: CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25. These candidate genes were involved in a number of metabolic processes that are possibly related to CH4 production. One of the most promising candidate genes (PKD1) was related to the development of the digestive tract. The results indicate that CH4 production is a highly polygenic trait. Pszczola et al., 2018[78]
A 1000-cow study across European countries revealed that the ruminant microbiomes can be controlled by the host animal. A 39- member subset of the core microbiome formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (CH4 emissions, rumen and blood metabolites, and milk production efficiency). Wallace et al.,

2019[79]

Methane measurements methods

Several factors influence the choice of measurement method such as cost, level of accuracy, precision, scope of application, and scale, which vary across disciplines (Cassandro et al., 2013[80]; Hammond et al., 2016A[81]; Garnsworthy et al., 2019[82]). For instance, genetic selection programs require CH4 measurements on thousands of related individuals under the environmental conditions in which the animals are expected to perform (Falconer and Mackay, 1996[83]). This can be challenging because dairy cattle perform in a wide range of conditions (e.g. grazing vs indoor housing). There are a number of different measurement methods currently being employed, each with advantages and disadvantages in terms of the factors listed above. The currently accepted and widely used measurement methods are listed and described below. The main features of methods for measuring CH4 output by individual animals are summarised in Table 3. Values for each feature are based on experience of experts in METHAGENE WG2 who have used the methods. All values are relative, and somewhat subjective, because absolute values will depend on installation and implementation of each method at different research centres. It should be noted that the measuring methods can be divided in two major sections: methods that measure the concentration and flux of CH4 (e.g. the respiration chamber), and methods that measure the flux of CH4 through the device (e.g. GreenFeed). This affects the useability of the methods for answering research questions – please see also the recommendations at the end of these guidelines.

Table 3. Summary of the main features of methods for measuring CH4 output by individual animals.
Method Purchase cost Running costs Labour Repeatability Behaviour alteration Throughput
Respiration chamber High High High High High Low
SF6 technique Medium High High Medium Medium Medium
Breath sampling during milking and feeding Low Low Low Medium None High
GreenFeed Medium Medium Medium Medium Medium Medium
Laser methane detector Low Low High Low Low-Medium Medium

Discussion of methods

SF6 vs Respiration Chamber

For large-scale evaluation of CH4 emissions by individual animals, the SF6 technique is more useful than respiration chambers. Animal behaviour and intake might be affected by wearing the apparatus, and by daily handling to exchange canisters, but the technique is considerably less intrusive than respiration chambers because cows remain in the herd. Labour and monetary costs for changing canisters each day and for lab analysis are high. Throughput is limited by the number of sets of apparatus available, handling facilities, labour, and the capacity of the lab for gas analysis. Animals need to be measured for 5 to 7 days, and it is recommended that group size should be less than 15 animals (Berndt et al., 2014[84]), so maximum throughput would be about 750 animals per year. The method may be better suited for in housed conditions because of the labour and the potential movement restriction of the animals due to wearing the apparatus.

Breath sampling during milking and feeding vs Respiration Chamber

For large-scale evaluation of CH4 emissions by individual animals, breath-sampling methods have significant advantages compared with other methods. Breath-sampling methods are non-invasive because, once installed, animals are unaware of the equipment and are in their normal environment. Animals follow their normal routine, which includes milking and feeding, so no training of animals, handling, or change of diet is required. Equipment is relatively cheap, although more expensive gas analysers are available, and running costs are negligible.

The compromise for non-invasiveness of breath-sampling is that concentrations of gases in the sampled air are influenced by cow head position relative to the sampling tube (Huhtanen et al., 2015[85]). The use of head position sensors and data filtering algorithms can remove the effects when the cow’s head is completely out of the feed bin (Difford et al., 2016[86]), but not within the feed bin. Consequently, sniffer measurements are more variable than flux methods, with factors like variable air flow in the barn increasing measurement error (imprecision), and head position, a highly repeatable character, inflating between-cow variability.

Using CO2 as a tracer gas partly addresses the issue but, because CO2 arises from metabolism as well as rumen fermentation, variability of CO2 emissions has to be considered. A further consideration is diurnal variation in breath concentrations of CH4 and CO2 because animals are spot-sampled at different times of day and night. Diurnal variation can be accounted for either by fitting a model derived from the whole group of animals, or by including time of measurement in the statistical model (Lassen et al., 2012[87]).

The number of observations per analyser is limited only by number of cows assigned to one automatic milking station or concentrate feeding station and length of time equipment is installed. Typically, each analyser will record 40 to 70 animals 2 to 7 times per day for 7 to 10 days, although the number of sampling stations per analyser can be increased by using an automatic switching system (Pszczola et al., 2017[88]). Throughput per analyser is likely to be 2,000 to 3,000 animals per year.

NDIR vs LMD

Both methods are low invasive. LMD needs larger labor force, wheras NDIR can be used during milking and feeding. According to Rey at al. (2019)[89], the repeatability of the CH4 concentration was greater for NDIR (0.42) than for LMD (0.23). Correlation between methods was moderately high and positive for CH4 concentration (0.73 and 0.74,respectively) and number of peaks (0.72 and 0.72, respectively), and the repeated measures correlation and the individual-level correlation were high (0.98 and 0.94, respectively). A high coefficient of individual agreement for the CH4 concentration (0.83) and the number of peaks (0.77) were observed between methods. The study suggests that methane concentration measurements obtained from NDIR and LMD cannot be used interchangeably. But the use of both methods could be considered for genetic selection purposes or for mitigation strategies only if sources of disagreement, which result in different between subject and within-subject variabilities, are identified and corrected for.

Greenfeed

A limitation of the GreenFeed system is that animals require training to use the system, although animals which have been trained to use the system will readily use it again (Velazco et al., 2014[90]). However, some animals will not use the system or will use it infrequently, and frequency of visits is affected by diet (Hammond et al., 2016B[91]). This can be a challenge when screening commercial herds for CH4 emission under genetic evaluation. On the other hand, animals seem to get used to the equipment rapidly, and the sound produced by the system is remembered by the animals easily (personal information Dr. Finocchiaro). Alternatively, as practised in Canada, the unit is moved to individual animals in a tie-stall setting multiple times a day (personal information Prof C.F. Baes). Thus, action of individual animals is not needed. The manufacturer recommends 15 to 25 animals per GreenFeed unit, and recordings are made typically for 7 days. If all animals visit the unit adequately, throughput per unit is likely to be 750 to 1,250 animals per year. Sebek et al. (2019A, B)[92] and Bannink et al. (2018)[93] showed the usefulness of the GreenFeed method in an on farm setting.

Laser Methane Detector

The LMD can be used in the animal’s normal environment, although for consistency restraint is required during measurement. Because the LMD measures CH4 in the plume originating from the animal’s nostrils, results can be affected by factors such as: distance from the animal; pointing angle; animal’s head orientation and head movement; air movement and temperature in the barn; adjacent animals; and operator variation (Sorg et al., 2017[94]). Operator variation is likely to be one of the biggest factors because the operator controls distance and pointing angle, and is responsible for ensuring the laser remains on target. The structure of the barn and the resulting ventilation conditions and wind speed at the location of the measurement are also considerable sources of variation in recorded CH4. Assuming operator fatigue does not limit measurements, each LMD could record up to 10 animals per hour. If each animal is recorded 3 times (on 3 consecutive days, for example, as in Mühlbach et al. (2018)[95]), throughput is likely to be up to 1000 animals per year.

Comparison of methods to measure methane

Proxies

Proxies discussion

Merging and sharing data in genetic evaluations

Recommendations

Conclusions

Sub-sections

Summary of Changes

Date of change Nature of change
March 2020 Draft from Feed & Gas WG put into standard template for ICAR Guidelines. Separate out EDGP database to become a standalone appendix.
April 2020 Edits and acknowledgements added by Feed & Gas WG.
May 2020 Approved by ICAR Board on 26th May subject to addition of disclaimer.

Disclaimer added as new chapter 2 - the fact specific device manufacturers are mentioned in these guidelines is in no way an endrosement of the devices or their accuracy by ICAR.

December 2023 Creation of Methane Emission for Genetic Evaluation Wiki Page.

References

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  2. ↑ Gerber, P.J., 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.
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  73. ↑ Lassen, J., and Løvendahl, P. 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959-1967.
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  82. ↑ Garnsworthy, P.C. Difford, G.F. Bell, M.J. Bayat, A.R. Huhtanen, P. Kuhla, B. Lassen, J. Peiren, N. Pszczola, M; Sorg, D. Visker, M.H., and Yan, T. 2019 Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle. Animals 9:837, 12p.
  83. ↑ Falconer, D., and Macka,y T. 1996. Introduction to quantitative genetics (4th edn). ISBN-13: 978-0582243026; ISBN-10: 0582243025
  84. ↑ Berndt, A., Boland, T.M., Deighton, M.H., Gere, J.I., Grainger, C., Hegarty, R.S., Iwaasa, A.D., Koolaard, J.P., Lassey, K.R., Luo D., Martin, R.J., Martin, C., Moate, P.J., Molano, G., Pinares-Patiño, C., Ribaux, B.E., Swainson, N.M., Waghorn, G.C., and Williams, S.R.O. 2014. Guidelines for use of sulphur hexafluoride (SF6) tracer technique to measure enteric methane emissions from ruminants. Pages 166. M. G. Lambert, ed. New Zealand Agricultural Greenhouse Gas Research Centre, New Zealand.
  85. ↑ Huhtanen, P., Cabezas-Garcia, E.H., Utsumi, S., and Zimmerman, S. 2015. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98:3394–3409. doi:10.3168/jds.2014-9118.
  86. ↑ Difford, G.F., Lassen, J., and Løvendahl, P. 2016. Interchangeability between methane measurements in dairy cows assessed by comparing precision and agreement of two non-invasive infrared methods. Comput. Electron. Agric. 124:220–226. doi:10.1016/j.compag.2016.04.010.
  87. ↑ Lassen, J., Lovendahl, P., and Madsen, J. 2012. Accuracy of noninvasive breath methane measurements using Fourier transform infrared methods on individual cows. J. Dairy Sci. 95:890-898.
  88. ↑ Pszczola, M., Rzewuska, K., Mucha, S., and Strabel, T. 2017. Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. J. Anim. Sci. 95:4813-4819. doi: 10.2527/jas2017.1842.
  89. ↑ Rey, J., Atxaerandio, R., Ruiz, R, Ugarte, E., Gonzalez-Recio, O., Garcia-Rodriguez, A., and Goiri, I. 2019. Comparison Between Non-Invasive Methane Measurement Techniques in Cattle. Animals 9(8): 563. https://doi.org/10.3390/ani9080563
  90. ↑ Velazco, J.I., Cottle, D.J., and Hegarty, R.S. 2014. Methane emissions and feeding behaviour of feedlot cattle supplemented with nitrate or urea. Anim. Prod. Sci. 54:1737–1740. doi:10.1071/AN14345.
  91. ↑ Hammond, K.J., Jones, A.K., Humphries, D.J., Crompton, L.A., and Reynolds, C.K. 2016B. Effects of diet forage source and neutral detergent fiber content on milk production of dairy cattle and methane emissions determined using GreenFeed and respiration chamber techniques. J. Dairy Sci. 99:7904–7917. doi:10.3168/jds.2015-10759.
  92. ↑ Sebek, L.B. 2019A. Project 11: Enterisch methaan: emissievariatie in de Nederlandse melkveestapel. 1 p. Wageningen : Wageningen University & Research.
  93. ↑ Bannink, A., Spek, J.W., Dijkstra, J., and Sebek, L.B. 2018. A Tier 3 Method for Enteric Methane in Dairy Cows Applied for Fecal N Digestibility in the Ammonia Inventory. In: Front. Sust. Food Syst. 2:66.
  94. ↑ Sorg, D., Difford, G.F., Mühlbach, S., Kuhla, B., Swalve, H.H., Lassen, J., Strabel, T., and Pszczola, M. 2017. Comparison of a laser methane detector with the GreenFeed and two breath analysers for on-farm measurements of methane emissions from dairy cows. Comp. Elec. Agric. 153:285-294.
  95. ↑ Mühlbach, S., Sorg, D., Rosner, F., Kecman, J., and Swalve, H.H. 2018. Genetic analyses for CH₄ concentrations in the breath of dairy cows measured on-farm with the Laser Methane Detector. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, Abstract No. 186, 11-16th February, Auckland, New Zealand.
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