Science International  Volume 5 Issue 4, 2017

Review Article

Mathematical Prediction Equations of Methane Emission from Dairy Cattle
Mostafa Sayed Abdellatif Khattab
Department of Dairy Sciences, National Research Centre, 12622 Dokki, Giza, Egypt

Several techniques were developed to estimate quantity of methane emissions, but these techniques are not practical at the farm conditions, so, attention were turned toward possible approaches to formulate a high accurate prediction model. Different studies were illustrated that amount digested feeds in the rumen, concentrate and roughage contents are the most important factors affecting methane production. The digestion feed in rumen and hydrogen produced could be estimated by VFA’s concentration, then it could help us to estimate. The dynamic and mechanistic models can be used to estimate methane emissions from ruminants. Their accuracy in prediction of methane production could be helpful to better estimate the contribution of ruminants to total global emission of methane. They also could be used to evaluate different strategies to reduce methane losses without affecting the metabolic efficiency of the whole rumen system. In conclusion, the developed equations could accurately and rapidly predict the CH4 production. Further in vitro and in vivo studies with a broader range of feedstuffs differing in constituents for each category are necessary to improve the accuracy and representation of the predictive equations before their practical application.
    How to Cite:
Mostafa Sayed Abdellatif Khattab , 2017. Mathematical Prediction Equations of Methane Emission from Dairy Cattle. Science International, 5: 133-141
DOI: 10.17311/sciintl.2017.133.141

Methane represents one of the major greenhouse gases which contributes to global warming1. FAO reported that agricultural activities are estimated to be responsible of 18.5% of the produced greenhouse gasses world-wide2. The estimated data showed that live stock and manure contribute by 27.5% of agricultural activities contribute in gas emmission (Table 1).

It has been estimated that methane produced from livestock represent 36% of total methane produced in the world. Estimates showed that an adult cattle produce 70 and 120 kg of methane/year2.

Ruminants are characterized by its ability to digest non-degradable cellulolytic materials by fermenting it in rumen (Fig. 1), depending on the anaerobic microbial community. The rumen is a unique organ characterized as an obligate anaerobic medium (no oxygen), pH ranging between (6-7), temperature of 30°C; which provide an ideal condition for its microbial habitats (bacteria, fungi, protozoa); to microbially digest consumed feeds (~ 9 h)3.

Rumen microbial population contains multiple genera and species of anaerobic bacteria (cellulolytic, hemicellulolytic, amylolytic, proteolytic, ammonia producers, vitamin synthesizers, methane producers and fungi).

Anaerobic digestion of feeds in the rumen (Fig. 2) supply the ruminant animal with energy sources as volatile fatty acids (acetate, propionate, butyrate, valerate, iso-butyrate and iso-valerate). Methane, CO2 and H2 are produced as secondary metabolic components.

In the rumen, carbohydrates (simple and complex) are hydrolyzed to 5- and 6-carbon sugars by microbial enzyme produced by microbial population. Sugars are fermented to VFA through multiple-step pathways that produce reducing equivalents (i.e., metabolic hydrogen), which can be summarized in the following4-6:

Glucose → 2 pyruvate+4H

Pyruvate+H2O → acetate+CO2+2H

Pyruvate+4H → propionate+H2O

2 Acetate+4H → butyrate+2H2O

Table 1: Contribution of agricultural activities on greenhouse gases emmission

Figure 1: Four compartment stomach of cows

Figure 2: Digestion of feed nutrients in the rumen

Figure 3: Fermentation schemes of ruminal bacteria. Interspecies hydrogen transfer and hydrogen utilization by methanogens are a primary means of reducing equivalent disposal

Due to anaerobic fermentation and metabolism in the rumen, hydrogen is considered the centric metabolite, which is utilized in different metabolic pathways such as bio-hydrogenation of unsaturated fatty acids or methanogenesis7.

Hydrogen production occurs in three key states in the rumen, these being hydrogen gas, reduced cofactors (such as NADH and NADPH) and as free protons8 (Fig. 3). The major part of H+ produced in the rumen is utilized in bio-hydrogenation of unsaturated fatty acids consumed in the diet, while the residuals H+ consumed by methanogens bacteria with CO2 and produce methane.

CO2+4 H2 → CH4+2 H2O

According to United Nation estimates, the world population will reach 8.9 billion habitants in 20509. In this context, this contentious increase in population need parallel increase in food production, which will lead to an environmental pressure due to greenhouse gas emissions.

Different in vitro and in vivo experiments were carried out to estimate methane production from rumen fermentation under different conditions of nutritional strategies. The quantity of produced CH4 in rumen depends on several factors such as: Daily feed intake, chemical composition, concentrate: Roughage ratio, physical properties of diet and feed rumen digestibility10. Different studies showed that there is positive relationship between cellulolytic materials in diet and methane production; it well known that the increase of fibers in diets led to increase acetic acid in rumen and methane produced, while propionic acid was decreased8,10. In the other hand, the relationship between diet concentrate content and propionic acid are negatively associated with methane production in the rumen.

Different studies have been carried out to estimate methane production in dairy cows10-16.

Literature illustrated that several models have been developed to maximize the accuracy of methane prediction produced in the rumen. The differences between these models are dependent on model variables, objectives and structure17-21. Different in vitro comparative studies were carried out to evaluate the prediction models using simulating ruminal techniques for different diets, have already been completed22-25.

Theoretically, prediction of methane emission could be known by the breakdown and flow of nutrients in the digestive system10,26. Pathways of feed in the rumen are:

•  Digested by ruminal microflora to generate volatile fatty acids (VFA’s) (source of energy for ruminant) and hydrogen (H2) as end products
•  Utilized by rumen microflora for microbial biosynthesis
•  The residual un-degraded feed nutrients are outflow from the rumen

Different prediction models of methane prediction in the rumen could be categorized in 2 main groups:

•  Empirical (statistical) models (Regression equations)
•  Dynamic and mechanistic models

Regression equation models for methane prediction: Several regression equations were developed to estimate feed energy lost as methane by ruminants. Empirical (statistical) models that relate nutrient intake to CH4 output directly. Different prediction equations were suggested to predict methane to elucidate the factors affecting the CH4 output of feedstuffs or diets27,28. The concept of regression equation models estimate production of methane depends on different variables such as dry matter intake (DMI), gross energy (GE) and digestible energy (DE).

Blaxter and clapperton prediction model: The equation of Blaxter and Clapperton29 was derived from series in vivo methane measurements experiments on sheep and cattle fed different diets. The equation depends on digestible energy and feed intake as variables relative to maintenance:

CH4 = 1.30+0.122 D+L (2.37-0.05 D)

D = Digestibility at the maintenance level of feeding
L = Level of feeding

Different application and evaluation trials of Blaxter and Clapperton equation showed an overestimation of methane production by 0.32 Mcal/day11, this inaccurate estimation may returns to equation does not account for another important factor such as mentioned previously. Also, the equation cannot be applied to a wide range of feedstuffs varying in chemical constituents28,30.

Moe and tyrrell prediction model: Moe and Tyrrell31 used data from cattle fed high-quality diets in order to derive their equation, which related the produced methane (Mcal/day) to some variable which were soluble residue intake (kg/day), hemicellulose and cellulose intake from cattle fed high quality dairy diets. There regression equation for prediction methane output was:

CH4 (g/day) = 33.0+104.6×digested cellulose (kg/day)
+38.5×digested hemicellulose (kg/day)
+20.5×digested neutral detergent-soluble
residue (kg/day)

Originally, Moe and Tyrrell31 meant with soluble residue a residual of subtracting crude protein and ether extract from the neutral-detergent solubles. Moe and Tyrrell31, reported that the produced methane of soluble carbohydrate fermentation is less than structural cell wall carbohydrates.

The accuracies of Moe and Tyrrell31 equation containing descriptors of dietary carbohydrate intake varies in error of prediction within unstable range of dairy cattle diets. Wilkerson et al.32 suggested that Moe and Tyrrell’s model was a development of Blaxter and Clapperton equation, because Moe and Tyrrell incorporated feed characteristics in their equation. The variables most effective in accurately predicting methane production include the digestibilities of fiber components such as cellulose, hemicellulose and neutral detergent soluble33.

Other evaluation and comparison studies for Moe and Tyrrell31 model showed that the model overestimate the prediction of methane produced in the rumen because the model because the model does not account for possible effects of other factors ether extract content and its effect of ruminal fermentation34 and lower correlation factor with in vivo methane measured35.

Sequential prediction models: The objective of the sequential approach for prediction methane production is similar to the IPCC36, which is based on different levels of available information. The major level of sequential approach was mainly, gross energy, dietary and animal levels37:

•  In the first level, methane are predicted using the gross energy intake
•  In the second level, diet characteristics [CP (%), NDF (%), ADF (%), EE (%) and ME], as well as GEI, are potential predictors
•  In the third level, milk composition (fat, protein and non-fat soluble) and animal information (body weight and breed), as well as variables from the dietary level are potential predictors

GEI was selected as a measure of animal’s feed intake to be consistent and comparable with current national greenhouse gas inventories and to examine methane emissions from an energy loss perspective.

Yan et al.12 developed a prediction model using more variables than used in the previous models. The developed model were carried out using a data of total 322 cattle roughage dry matter (DM) intake as a proportion of total DM intake (SDMI/TDMI), total acid detergent fiber (ADF) intake as a proportion of TDMI (TADFI/TDMI) or roughage ADF intake as a proportion of TADFI (SADFI/TADFI) and feeding level above maintenance (FL-1).

Yan et al.12 were illustrated the step of creating methane prediction equation and the relationship between methane energy (CH4-E) and energy intake and other variables on the combined data of dairy and beef cattle in four steps:

•  First step, relating CH4-E to total GE intake (GEI) and digestible energy (DE) intake (DEI) by using the linear regression technique:

CH4-E = a+b . intake

•  Second step, calculated methane energy (CH4-E/GEI, CH4-E/DEI) from first step were then each related to feeding level (FL), apparent energy digestibility, roughage DM intake (SDMI) as a proportion of total DM intake (TDM) (SDMI/TDMI), total ADF intake (TADFI) as a proportion of TDMI (TADFI/TDMI) and roughage ADF intake (SADFI) as a proportion of TADFI (SADFI/TADFI):

CH4-E/intake = a+b .digestibility

CH4-E/intake = a+b. (FL-1)

CH4-E/intake = a+b. (dietary factor)

•  Third step, methane energy values calculated from second step related to energy intake (GE or DE) and FL above maintenance (FL-1) or dietary factor (TADFI/TDMI, SDMI/TDMI or SADFI/TADFI):

CH4-E = a+b . intake+c . (FL-1)

CH4-E = a+intake . [b+c . (dietary factor]

•  Finally, CH4-E was predicted using the above three groups of variables {energy intake (GE or DE), feeding level (FL-1) and dietary factor (TADFI/TDMI, SDMI/TDMI or SADFI/TADFI( }:

CH4-E = a+intake . [b+c . (dietary factor)]+d (FL-1)

Results showed two equations gave accurate prediction:

CH4-E (MJ/day) = DEI (0.094 + 0.028 SADFI/TADFI)-2.453 (FL-1)

CH4-E (MJ/day) = DEI (0.096+0.035 SDMI/TDMI)-2.298 (FL-1)

The relationship between predicted methane and actual methane were highly significant (p<0.001) and R2 values was 0.92 for each equation.

Another study for creating prediction equation of methane production was carried out by Moraes et al.37 using database containing 2574 indirect respiration calorimetry records of dairy and beef cattle in 62 studies conducted from 1963-1995 in the former USDA Energy Metabolism Unit at Beltsville, Maryland.

Model development was conducted in a sequential approach, with increasing model complexity at each level. Researchers aimed to similar the sequential approach was similar to the IPCC36, which is based on Three complexity levels namely gross energy, diet and animal levels:

•  In the first level, emissions are predicted depending on the animal’s gross energy intake
•  In the second level, diet traits (fiber fractions, crude protein, ether extract and metabolizable energy), as well as gross energy intake are used to predict
•  In the third level, milk composition (fat, protein and nonfat soluble) and animal information (body weight and breed), as well as variables from the dietary level are potential predictors

Gross energy intake was mainly selected as a measure of animal’s feed consuming to be consistent and comparable and to examine methane emissions from an energy loss perspective.

Models calculated methane production (MJ/day):

CH4 = 3.247+0.043×GEI
CH4 = 0.225+0.042×GEI+0.125×NDF-0.329×EE
CH4 = -9.311+0.042×GEI+0.094×NDF-0.381×EE+0.008×BW = 1.621×MF

Results of prediction using the equation showed that gross energy intake was the important key variable in predicting methane emissions and was present in the selected models across all complexity levels and data sets.

Castro-Montoya et al., mechanistic model: Recently, many efforts were carried out to investigate the correlation between ruminant animal products especially milk and the methane produced through fermentation in the rumen of the producing animals. Different studies tried to correlate between milk fatty acids (MFAs) and methane production38-43. Castro-Montoya et al.15 developed a model by inclusion dry matter intake (DMI) to improve the prediction of CH4 emissions g/day) compared with models based solely on MFAs. Results showed that combining milk fatty acids and DMI present better prediction than predictions based on DMI only. Methane emissions from lactating ruminant have been traditionally associated with MFA by regressing the former on the MFAs proportions intending to generate a prediction of the actual amount of CH4 emitted by an animal44.

Correlations between CH4 and individual MFAs were generally weak through different data set has been observed in different studies18,40,41.

Mohammed et al.40 and Rico et al.41 reported that the highest correlation between CH4 produced (g/day) and cis-9 C17:1 (Table 2).

In agreement of Castro-Montoya et al.15 model, Rico et al.41 reported that including DMI and MFA in the model was better than other model based on MFAs only (R2 = 0.8 v. 0.58).

Table 2: Pearson’s correlations between methane emissions and portions of individual milk fatty acids (MFA) (g/100 MFA)

The Castro-Montoya et al.15 model approach depended on multiple linear regression by using data set (n = 140).

The regression analysis was done by fitting a generalized linear mixed model, which deals with correlations from repeated measurements and/or shared random effects from an experiment45:

CH4 (g/day) = 471-137×cis-13 C16:1-824×trans-14
C16:1+138×trans-10 C18:1-280×trans-12 C18:1-325
×trans-11, cis-15 C18:2

CH4 (g/day) = 282+16.6×DMI-89×C17-32.3×trans-C18:1-
146× cis-13 C16:1-117×iso C17:0

Nevertheless, two main weaknesses could be identified in previous studies: First, models have been developed depending on limited number of observations or from experiments including treatments directly influencing the MFA profile potentially holding a powerful relation with CH4 emissions.

Second, models were not validated on experiments to evaluate if it could determine coefficients reflect the variation explained by MFAs as well as random effects of experiment15.

Moreover, Milk fatty acid might be an alternative high characterize from low emitters. The key factor of utilizing MFAs as effective variable is their distribution profile, rather than their concentrations46,47.

From current approaches, milk fatty acids appear to have a little potential to predict CH4 emitted by dairy cows.

Dynamic and mechanistic models
Description of the mechanistic models: The dynamic and mechanistic for predicting models of methane production from rumen were structured on models which predict rates and patterns of nutrient absorption. The variables included in prediction models are describing soluble and insoluble dietary nutrients, fermentation intermediates, end products, degradation rates for hemicellulose and cellulose. The rumen prediction models simulate the effect of rumen kinetics such as pH on microbial maintenance requirements, proportions of VFA produced and rates of hemicellulose and cellulose degradation48. All that variable to help increase the prediction accuracies which based on hydrogen balance. The produced hydrogen as intermediate in the metabolism are used for microbial cell growth and production and for biohydrogenation of unsaturated fatty acids consumed in the diet. The remaining hydrogen is utilized by methanogenic archaea in reduction of carbon dioxide to methane49.

Baldwin et al., mechanistic model: As described previously, the produced methane in the rumen depends on hydrogen produced or used during the formation of VFA which could be calculated according to rumen fermentation stoichiometries5, the hydrogen quantities consumed in biosynthesis of microbial cell components were calculated based on equations reported by Reichl and Baldwin50. While, the amounts of hydrogen used for saturating dietary unsaturated fatty acids consumed is calculated according to: The hydrolysis of lipid releases 1 mol of glycerol and 1.8 mol of long-chain fatty acid per mole of lipid50.

The steps of ruminal methane production calculation are:

Amount of hydrogen resulting from fermentation of carbohydrates (Hcar) to VFA:

Hcar = (Accar×2.0)-(Prcar ×1.0)+(Bucar×2.0)-(VLcar×1.0)

where, Accar, Prcar, Bucar, VLcar are the amounts (mol/day) of acetate, propionate, butyrate and valerate produced from fermentation of carbohydrates, respectively.

Quantity of hydrogen resulting from fermentation of amino acids (Haa) to VFA:

Haa = (Acaa×2.0)-(Praa×1.0)+(Buaa×2.0)-(VLaa×1.0)

where, Acaa, Praa, Buaa and VLaa are the amounts (mol/day) of acetate, propionate, butyrate and valerate produced from fermentation of amino acids, respectively.

Amount of hydrogen used for biosynthesis of microbial cell components (Hmg):

Hmg = (MG1×-0.42)+(MG2×2.71)

where, MG1 and MG2 are the amounts of microbes (kg/day) growing with and without preformed amino acids, respectively. The coefficients -0.42 and 2.71 are moles of hydrogen kg‾1 of microbes.

Amount of hydrogen used for biohydrogenation of unsaturated fatty acids (HFA):

HFA = 1.8×Lipiding×HSFA

where, Lipiding is the amount of lipids ingested (mol/day) and HSFA represents moles of hydrogen used for saturation of 1 mole of unsaturated fatty acids.

Calculation of hydrogen balance in the rumen:

Hrumen = Hcar+Haa-Hmg-HFA

Calculation of ruminal methane production:

CH4rumen (mol/day) = Hrumen/4.0

where, 4.0 is mole of hydrogen used for the production of 1 mole of CH4

And, finally:

CH4rumen (Mcal/day) = CH4rumen×0.211

where, 0.211 is heat combustion of methane in mega calories mol‾1.

The current updated model of Baldwin et al.51 requires more extensive feed evaluation characterization than other models such as the chemical composition of the diets especially cell walls (cellulose, hemicellulose and lignin), protein (soluble and insoluble fractions), non-protein nitrogen, starch and its solubility, soluble sugars, pectin, organic acids, lipids, lactate and VFA. One of the advantages of Baldwin et al.51 model is most of the variable used for prediction can be easily found in tables of feed composition or in the literature. While some other variables are scarce (pectin, organic acid concentrations). Some evaluating studies of updated Baldwin et al.51 model stated overestimation by 0.93 Mcal/day which could be due to an incorrect estimation of the pattern of VFA produced in the rumen11.

Using prediction method for estimating methane produced from dairy cattle could be an accurately and rapidly method to evaluate feeds and animal performance which could be helpful to create an effective strategies for reducing methane emission from livestock animals. While, further in vitro and in vivo studies with a broader range of feedstuffs differing in constituents for each category are necessary to improve the accuracy and representation of the predictive equations before their practical application.


•  This study was carried out to review the mathematical efforts to create a prediction equation for methane production from dairy cattle
•  Prediction equations need further studies to increase the accuracy of the prediction equation under different production conditions and variables


  1. Johnson, K.A. and D.E. Johnson, 1995. Methane emissions from cattle. J. Anim. Sci., 73: 2483-2492

  2. FAO., 2006. Livestock Long Shadow: Environmental Issues and Options. Food and Agriculture Organization, Rome, Italy, ISBN: 9789251055717, Pages: 390.

  3. Church, D.C., 1988. The Ruminant Animal: Digestive Physiology and Nutrition. 2nd Edn., Prentice Hall, Englewood Cliffs, New Jersey, USA.

  4. Hungate, R.E., 1966. The Rumen and its Microbes. 1st Edn., Academic Press, London, Pages: 553.

  5. Czerwaski, J.W., 1986. An Introduction to Rumen Studies. Pergamon Press, Oxford, Pages: 233.

  6. Moss, A.R., J.P. Jouany and J. Newbold, 2000. Methane production by ruminants: Its contribution to global warming. Annales Zootechnie, 49: 231-253

  7. Van Lingen, H.J., C.M. Plugge, J.G. Fadel, E. Kebreab, A. Bannink and J. Dijkstra, 2016. Thermodynamic driving force of hydrogen on rumen microbial metabolism: A theoretical investigation. PLoS ONE, Vol. 11. 10.1371/journal.pone.0161362

  8. Hegarty, R.S. and R. Gerdes, 1999. Hydrogen production and transfer in the rumen. Recent Adv. Anim. Nutr. Aust., 12: 37-44

  9. United Nations, 2004. World Population to 2300. United Nations Publications, New York, USA., ISBN-13: 9789211514018, Pages: 240.

  10. Brask, M., M.R. Weisbjerg, A.L.F. Hellwing, A. Bannink and P. Lund, 2015. Methane production and diurnal variation measured in dairy cows and predicted from fermentation pattern and nutrient or carbon flow. Animal, 9: 1795-1806

  11. Benchaar, C., J. Rivest, C. Pomar and J. Chiquette, 1998. Prediction of methane production from dairy cows using existing mechanistic models and regression equations. J. Anim. Sci., 76: 617-627

  12. Yan, T., R.E. Agnew, F.J. Gordon and M.G. Porter, 2000. Prediction of methane energy output in dairy and beef cattle offered grass silage-based diets. Livest. Prod. Sci., 64: 253-263

  13. Mills, J.A.N., J. Dijkstra, A. Bannink, S.B. Cammell, E. Kebreab and J. France, 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

  14. 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

  15. Castro-Montoya, J.M., N. Peiren, J. Veneman, B. de Baets, S. de Campeneere and V. Fievez, 2017. Predictions of methane emission levels and categories based on milk fatty acid profiles from dairy cows. Animal, 11: 1153-1162

  16. Cabezas-Garcia, E.H., S.J. Krizsan, K.J. Shingfield and P. Huhtanen, 2017. Between-cow variation in digestion and rumen fermentation variables associated with methane production. J. Dairy Sci., 100: 4409-4424

  17. Sauvant, D., 1988. [Modelling of digestion in the rumen]. Reprod. Nutr. Dev., 28: 33-58, (In French)

  18. Dijkstra, J. and J. France, 1995. Modelling and methodology in animal science. Proceedings of the 4th International Workshop on Modelling Nutrient Utilisation in Farm Animals, October 3-5, 1994, Foulum, Denmark, pp: 9-18.

  19. Sauvant, D., 1997. Rumen Mathematical Modelling. In: The Rumen Microbial Ecosystem, Hobson, P.N. and C.S. Stewart (Eds.). Chapman and Hall, London, UK., ISBN: 978-94-010-7149-9, pp: 685-708.

  20. Tedeschi, L.O., D.G. Fox, R.D. Sainz, L.G. Barioni, S.R. de Medeiros and C. Boin, 2005. Mathematical models in ruminant nutrition. Sci. Agric., 62: 76-91

  21. Huhtanen, P., A. Seppala, M. Ots, S. Ahvenjarvi and M. Rinne, 2008. In vitro gas production profiles to estimate extent and effective first-order rate of neutral detergent fiber digestion in the rumen. J. Anim. Sci., 86: 651-659

  22. Ramangasoavina, B. and D. Sauvant, 1993. [Comparative validation of 3 models of ruminal digestion to predict the duodenal N microbial flow]. Annales Zootechnie, 42: 164-165, (In French)

  23. Kohn, R.A., R.C. Boston, J.D. Ferguson and W. Chalupa, 1995. The integration and comparison of dairy cow models. Proceedings of the 4th International Workshop on Modelling Nutrient Utilisation in Farm Animals, October 3-5, 1994, Foulum, Denmark, pp: 117-128.

  24. Bannink, A. and H. de Visser, 1997. Comparison of mechanistic rumen models on mathematical formulation of extramicrobial and microbial processes. J. Dairy Sci., 80: 1296-1314

  25. Bannink, A., H. de Visser and A.M. van Vuuren, 1997. Comparison and evaluation of mechanistic rumen models. Br. J. Nutr., 78: 563-581

  26. Young, K., 2013. Methane prediction by nutrient profiles in ruminal continuous cultures fed an all forage diet of Bermuda grass or annual ryegrass. Master Thesis, Graduate School of Clemson University, Clemson, SC., USA.

  27. Singh, S., B.P. Kushwaha, S.K. Nag, A.K. Mishra, A. Singh and U.Y. Anele, 2012. In vitro ruminal fermentation, protein and carbohydrate fractionation, methane production and prediction of twelve commonly used Indian green forages. Anim. Feed Sci. Technol., 178: 2-11

  28. Ramin, M. and P. Huhtanen, 2013. Development of equations for predicting methane emissions from ruminants. J. Dairy Sci., 96: 2476-2493

  29. Blaxter, K.L. and J.L. Clapperton, 1965. Prediction of the amount of methane produced by ruminants. Br. J. Nutr., 19: 511-522

  30. Chen, C.N., T.T. Lee and B. Yu, 2016. Improving the prediction of methane production determined by in vitro gas production technique for ruminants. Ann. Anim. Sci., 16: 565-584

  31. Moe, P.W. and H.F. Tyrrell, 1979. Methane production in dairy cows. J. Dairy Sci., 62: 1583-1586

  32. Wilkerson, V.A., D.P. Casper and D.R. Mertens, 1995. The prediction of methane production of Holstein cows by several equations. J. Dairy Sci., 78: 2402-2414

  33. Knapp, J.R., G.L. Laur, P.A. Vadas, W.P. Weiss and J.M. Tricarico, 2014. Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci., 97: 3231-3261

  34. McGinn, S.M., D. Chen, Z. Loh, J. Hill, K.A. Beauchemin and O.T. Denmead, 2008. Methane emissions from feedlot cattle in Australia and Canada. Aust. J. Exp. Agric., 48: 183-185

  35. Kebreab, E., K.A. Johnson, S.L. Archibeque, D. Pape and T. Wirth, 2008. Model for estimating enteric methane emissions from united states dairy and feedlot cattle. J. Anim. Sci., 86: 2738-2748

  36. IPCC., 2006. IPCC guidelines for national greenhouse gas inventories. National Greenhouse Gas Inventories Programme, Institute for Global Environmental Strategies, Japan.

  37. Moraes, L.E., A.B. Strathe, J.G. Fadel, D.P. Casper and E. Kebreab, 2014. Prediction of enteric methane emissions from cattle. Global Change Biol., 20: 2140-2148

  38. Chilliard, Y., C. Martin, J. Rouel and M. Doreau, 2009. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil and their relationship with methane output. J. Dairy Sci., 92: 5199-5211

  39. Dijkstra, J., S.M. van Zijderveld, J.A. Apajalahti, A. Bannink and W.J.J. Gerrits et al., 2011. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim. Feed Sci. Technol., 166: 590-595

  40. Mohammed, R., S.M. McGinn and K.A. Beauchemin, 2011. Prediction of enteric methane output from milk fatty acid concentrations and rumen fermentation parameters in dairy cows fed sunflower, flax, or canola seeds. J. Dairy Sci., 94: 6057-6068

  41. Rico, D.E., P.Y. Chouinard, F. Hassanat, C. Benchaar and R. Gervais, 2016. Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources. Animal, 10: 203-211

  42. Van Lingen, H.J., L.A. Crompton, W.H. Hendriks, C.K. Reynolds and J. Dijkstra, 2014. Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle. J. Dairy Sci., 97: 7115-7132

  43. Castro-Montoya, J.M., S. de Campeneere, B. de Baets and V. Fievez, 2016. The potential of milk fatty acids as biomarkers for methane emissions in dairy cows: A quantitative multi-study survey of literature data. J. Agric. Sci., 154: 515-531

  44. De Haas, Y., J.J. Windig, M.P.L. Calus, J. Dijkstra, M. de Haan, A. Bannink and R.F. Veerkamp, 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J. Dairy Sci., 94: 6122-6134

  45. Pinheiro, J.C. and D.M. Bates, 2000. Linear Mixed-Effects Models: Basic Concepts and Examples. In: Mixed-Effects Models in S and S-PLUS, Pinheiro, J.C. and D.M. Bates (Eds.). Chapter 1, Springer, New York, USA., pp: 3-56.

  46. Mertens, B., N. Boon and W. Verstraete, 2005. Stereospecific effect of hexachlorocyclohexane on activity and structure of soil methanotrophic communities. Environ. Microbiol., 7: 660-669

  47. Marzorati, M., L. Wittebolle, N. Boon, D. Daffonchio and W. Verstraete, 2008. How to get more out of molecular fingerprints: Practical tools for microbial ecology. Environ. Microbiol., 10: 1571-1581

  48. Argyle, J.L. and R.L. Baldwin, 1988. Modeling of rumen water kinetics and effects of rumen pH changes. J. Dairy Sci., 71: 1178-1188

  49. Ellis, J.L., J. Dijkstra, A. Bannink, E. Kebreab and S. Archibeque et al., 2014. Improving the prediction of methane production and representation of rumen fermentation for finishing beef cattle within a mechanistic model. Can. J. Anim. Sci., 94: 509-524

  50. Reichl, J.R. and R.L. Baldwin, 1975. Rumen modeling: Rumen input-output balance models. J. Dairy Sci., 58: 879-890

  51. Baldwin, R.L., J. France, D.E. Beever, M. Gill and J.H. Thornley, 1987. Metabolism of the lactating cow: III. Properties of mechanistic models suitable for evaluation of energetic relationships and factors involved in the partition of nutrients. J. Dairy Res., 54: 133-145


Science International © 2017