Section 20: Methane determining factors

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Diet and rumen microbiota

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

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[1];

Cottle et al., 2011[2]; Knapp et al., 2014[3]; O’Neill et al., 2011[4]; Sauvant et al., 2011[5]

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[6]
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[7]
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[8]
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[9]
Methane linearly increased with NDF intake for cows together with their calves independent of the breed. Estermann et al., 2002[10]
Enteric CH4 could be predicted with the equation:

Hindrichsen et al., 2005[11]
The higher the percentage concentrate the lower Ym. Zeitz et al., 2012[12]
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;[13]

Jayanegara et al.[14], 2012; Zmora et al., 2012[15]; Cieslak et al., 2013[16]; Guyader et al., 2014[17]

Plant essential oils have been shown as promising feed additives to mitigate CH4 and ammonia emission, but results were inconsistent. Cobellis et al., 2016;[18]

Moate et al., 2011[19]

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[20];

van Zijderveld et al., 2011[21]

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[22]
Implementing good grazing management reduced gross energy intake loss as CH4 by 14%. Wims et al., 2010[23]

Table 1. Methane determining factors related to diet and rumen microbiota.

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[24]). 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[25]). Table 2 contains information of heritability of traits related to CH4 production.

Factors Reference
List with several h2 Pickering et al., 2015[26]
List with several h2 MPWG White paper Dec 18[27]
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[28];

Garnsworthy et al., 2011B[29]

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[30]
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[31]
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[32]
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[33]
Milk production and CH4 emissions of dairy cows seemed to be influenced by the temperature humidity index. Vanrobays et al., 2013A[34]
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[35]
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[36]
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[37]
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)[38]
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[39]
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[40]
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[41]
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[42]

Table 2. Heritability information of methane-related traits and measurements.

  1. ↑ Beauchemin, K.A., McAllister, T.A., and McGinn, S.M. 2009. Dietary mitigation of enteric methane from cattle. CAB Rev.: Perspect. Agric., Vet. Sci., Nutr. Nat. Res. 4:1–18.
  2. ↑ Cottle, D.J., Nolan, J.V., and Wiedemann, S.G. 2011. Ruminant enteric methane mitigation: A review. Anim. Prod. Sci. 51:491–514. doi:10.1071/AN10163.
  3. ↑ Knapp, J.R., Laur, G.L., Vadas, P.A., Weis,s W.P., and Tricarico, J.M. 2014. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231-3261.
  4. ↑ O’Neill, B.F., Deighton, M.H., O’Loughlin, B.M., Mulligan, F.J., Boland, T.M., O’Donovan, M., and Lewis, E. 2011. Effects of a perennial ryegrass diet or total mixed ration diet offered to spring-calving Holstein-Friesian dairy cows on methane emissions, dry matter intake, and milk production. J. Dairy Sci. 94:1941 – 1951
  5. ↑ Sauvant, D., Giger-Reverdin, S., Serment, A., and Broudiscou, L. 2011. Influences des régimeset de leur fermentation dans le rumen sur la production de méthane par les ruminants. INRA Prod. Anim. 24:433–446.
  6. ↑ Garnsworthy, P.C. 2004. The environmental impact of fertility in dairy cows: a modelling approach to predict methane and ammonia emissions. Anim. Feed Sci. Technol. 112:211-223.
  7. ↑ Blaxter, K.L., and Clapperton, J.L. 1965. Prediction of the amount of methane produced by ruminants. Br. J. Nutr.19:511–522.
  8. ↑ Jentsch, W., Schweigel, M., Weissbach, F., Scholze, H., Pitroff, W., and Derno, M. 2007. Methane production in cattle calculated by the nutrient composition of the diet. Arch. Anim. Nutr. 61:10-19.
  9. ↑ Kirchgessner, M., Windisch, W., Müller, H. L., and Kreuzer, M. 1991. Release of methane and of carbon dioxide by dairy cattle. Agribiol. Res. 44:91-102.
  10. ↑ Estermann, B.L., Sutter, F., Schlegel, P.O., Erdin, D., Wettstein, H.R., and Kreuzer, M. 2002. Effect of calf age and dam breed on intake, energy expenditure, and excretion of nitrogen, phosphorus, and methane of beef cows with calves. J. Anim. Sci. 80:1124-1134.
  11. ↑ Hindrichsen, I.K., Wettstein, H.R., Machmüller, A., Jörg, B., and Kreuzer, M. 2005. Effect of the carbohydrate composition of feed concentratates on methane emission from dairy cows and their slurry. Environ. Monit. Assess., 107:329-350.
  12. ↑ Zeitz, J.O., Soliva, C.R., and Kreuzer, M. 2012. Swiss diet types for cattle: how accurately are they reflected by the Intergovernmental Panel on Climate Change default values? J. Int. Environ. Sci. 9(sup1):199-216.
  13. ↑ Beauchemin, K.A., Kreuze,r M., O’Mara, F., and McAllister, T.A. 2008. Nutritional management for enteric methane abatement: A review. Aust. J. Exp. Agric. 48:21–27.
  14. ↑ Jayanegara, A., Leiber, F., and Kreuzer, M. 2012. Meta‐analysis of the relationship between dietary tannin level and methane formation in ruminants from in vivo and in vitro experiments. J. Anim. Physiol. Anim. Nutr. 96:365-375.
  15. ↑ Zmora, P., Cieslak, A., Pers-Kamczyc, E., Nowak, A., Szczechowiak, J. and Szumacher-Strabel, M. 2012. Effect of Mentha piperita L. on in vitro rumen methanogenesis and fermentation, Acta Agr. Scan. Section A — Anim. Sci. 62:46-52, DOI: 10.1080/09064702.2012.703228.
  16. ↑ Cieslak, A., Szumacher-Strabel, M., Stochmal, A., nad Oleszek, W. 2013. Plant components with specific activities against rumen methanogens. Animal, 7(s2):253-265.
  17. ↑ Guyader, J., Eugène, M., Nozière, P., Morgavi, D.P., Doreau, M., and Martin, C. 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8:1816-1825.
  18. ↑ Cobellis, G., Trabalza-Marinucci, M. and Yu, Z. 2016. Critical evaluation of essential oils as rumen modifiers in ruminant nutrition: A review. Sci. Total Environ. 545: 556-568.
  19. ↑ Moate, P.J., Deighton, M.H., Williams, S.R.O., Pryce, J.E., Hayes, B.J., Jacobs, J.L., Eckard, R.J., Hannah, M.C. and Wales, W.J., 2016. Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions. Anim. Prod. Sci. 56:1017-1034.
  20. ↑ Van Zijderveld, S.M., Gerrits, W.J.J., Apajalahti, J.A., Newbold, J.R., Dijkstra, J., Leng, R A., and Perdok, H.B. 2010. Nitrate and sulfate: effective alternative hydrogen sinks for mitigation of ruminal methane production in sheep. J. Dairy Sci. 93:5856-5866.
  21. ↑ Van Zijderveld, S.M., Gerrits, W.J.J., Dijkstra, J., Newbold, J.R., Hulshof, R.B.A., and Perdok, H.B. 2011. Persistency of methane mitigation by dietary nitrate supplementation in dairy cows. J. Dairy Sci. 94:4028-4038.
  22. ↑ Schönhusen, U., Zitnan, R., Kuhla, S., Jentsch, W., Derno, M., and Voigt, J. 2003. Effects of protozoa on methane production in rumen and hindgut of calves around time of weaning. Arch. Anim. Nutr. 57:279-295.
  23. ↑ Wims, C.M., Deighton, M.H., Lewis, E., O’Loughlin, B., Delaby, L., Boland, T.M., and O’Donovan, M. 2010. Effect of pregrazing herbage mass on methane production, dry matter intake, and milk production of grazing dairy cows during the mid-season period. J. Dairy Sci. 93:4976 – 4985
  24. ↑ Donoghue, K.A., Bird-Gardiner, T., Arthur, P.F., Herd, R.M., and Hegarty, R.F. 2016. Genetic and phenotypic variance and covariance components for methane emission and postweaning traits in Angus cattle. J. Anim. Sci. 94:1438–1445. doi:10.2527/jas2015-0065.
  25. ↑ Pinares-Patiño C.S., Hickey, S.M., Young, E.A., Dodds, K.G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Pickering, N.K., and McEwan, J.C. 2013. Heritability estimates of methane emissions from sheep. Animal 7: 316–321.
  26. ↑ Pickering, N.K., Oddy, V.H., Basarab, J.A., Cammack, K., Hayes, B J., Hegarty, R.S., McEwan, J.C., Miller, S., Pinares, C., and de Haas, Y. 2015. Invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9:1431-1440.
  27. ↑ MPWG White paper Dec 18. http://www.asggn.org/publications,listing,95,mpwg-white-paper.html. Pickering, N.K., de Haas, Y., Basarab, J., Cammack, K., Hayes, B., Hegarty, R.S., Lassen, J., McEwan, J.C., Miller, S., Pinares-Patiño, C.S., Shackell, G., Vercoe, P. and Oddy, V.H. 2013.
  28. ↑ Garnsworthy, P.C., Craigon, J., Hernandez-Medrano, J.H. and Saunders, H. 2012A. On-farm methane measurements during milking correlate with total methane production by individual dairy cows. J. Dairy Sci. 95:3166-3180.
  29. ↑ Garnsworthy, P.C., Craigon, J., Hernandez-Medrano, J.H., and Saunders, N. 2012B. Variation among individual dairy cows in methane measurements made on farm during milking. J. Dairy Sci. 95:3181–3189.
  30. ↑ Mills, J.A.N., Dijkstra, J., Bannink, A., Cammell, S.B., Kebreab, E., and France, J. 2001. A mechanistic model of whole-tract digestion and methanogenesis in the lactating dairy cow: model development, evaluation, and application. J. Anim. Sci. 79:1584-1597.
  31. ↑ 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.
  32. ↑ Kandel, P.B., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Dardenne, P., Lewis, E., Buckley, F., Deighton, M.H., McParland, S. and Gengler, N., 2013. Genetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows. J. Dairy Sci. 95(E-1):p.388.
  33. ↑ Kandel, P.B., Vanderick, S., Vanrobays, M.L., Vanlierde, A., Dehareng, F., Froidmont, E., Soyeur,t H., and Gengler, N. 2014B. Consequences of selection for environmental impact traits in dairy cows. In: 10th World Congress on Genetics Applied to Livestock Production (WCGALP), 17-22 August, 2014. Vancouver, Canada.
  34. ↑ Vanrobays, M.-L., Gengler, N., Kandel, P.B., Soyeurt, H., and Hammami, H. 2013A. Genetic effects of heat stress on milk yield and MIR predicted methane emissions of Holstein cows. 64th Annual meeting of the European Federation of Animal Science, p498
  35. ↑ 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.
  36. ↑ 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.
  37. ↑ Breider, I.S., Wall, E., Garnsworthy, P.C. 2019. Short communication: Heritability of methane production and genetic correlations with milk yield and body weight in Holstein-Friesian dairy cows, J. Dairy Sci. 102: 7277-7281.
  38. ↑ Lopez-Paredes, J., Goiri, I., Atxaerandio, R., García-Rodríguez, A., Ugarte, E., Jiménez-Montero, J.A., Alenda, R and  González-Recio, O. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection (i): Genetic parameters of direct methane using non-invasive methods and its proxies. J. Dairy Sci. 103.
  39. ↑ Bittante, G., and Cecchinato, A. 2020. Heritability estimates of enteric methane emissions predicted from fatty acid profiles, and their relationships with milk composition, cheese-yield and body size and condition, It. J. An. Sci. 19:114-126, DOI: 10.1080/1828051X.2019.1698979
  40. ↑ Cassandro, M., Cecchinato, A., Battagin, M., Penasa, M., 2010. Genetic parameters of predicted methane production in Holstein Friesian cowsIn: Proc. 9th World Congr. on Genetics Applied to Livestock Production, Leipzig, Germany. . Page 181
  41. ↑ Pszczola, M., Strabel, T., Mucha, S., and Sell-Kubiak, E. 2018. Genome-wide association identifies methane production level relation to genetic control of digestive tract development in dairy cows. Scientific Rep. 8 (1), 15164 https://doi.org/10.1038/s41598-018-33327-9
  42. ↑ Wallace, R.J., Sasson, G., Garnsworthy, P.C., Tapio, I., Gregson, E., Bani, P., Huhtanen, P., Bayat, A.R., Strozzi, F., Biscarini, F., Snelling, T.J., Saunders, N., Potterton, S.L., Craigon, J., Minuti, A., Trevisi, E., Callegari, M.L., Cappelli, F.P., Cabezas-Garcia, E.H., Vilkki, J., Pinares-Patino, C., Fliegerov, K.O., Mrazek, J., Sechovcova, H., Kope, J., Bonin, A., Boyer, F., Taberlet, P., Kokou, F., Halperin, E., Williams, J.L., Shingfield, K.J., and Mizrahi, I. 2019. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5:(7):eaav8391. doi 10.1126/sciadv.aav8391.
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