**Reducing Enteric Methane Losses from Ruminant Livestock – Its Measurement, Prediction and the Influence of Diet**

M. J. Bell and R. J. Eckard

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50394

## **1. Introduction**

134 Livestock Production

2009.pdf. Accessed 2012 Mar 6.

2011.pdf. Accessed 2012 May 16.

Ganadero: 16-19.

[29] SENASA (2011) Movimientos de Ganado Bovino Año 2010. Available:

[30] SENASA (2010) Movimiento de Ganado Bovino Año 2009. Available:

[32] Halle A G (2009) Ganadería; Análisis de Coyuntura Junio 2009. Available:

[34] IPCVA (2011) Argentina. Exportaciones de Carne Vacuna. Available:

[38] MAGYP (2011) Sistema Integrado de Información Agropecuaria. Available:

Agrarias, Universidad Nacional de Cuyo. 43 (2): 21-34. Available:

Relaciones Ganaderas. Suplemento Ganadero: 28.

catalog/product\_info.php?products\_id=26023

nal%20de%20Poblaci%F3n. Accessed 2012 May 14.

Suplemento Ganadero: 20-22.

50.

http://www.senasa.gov.ar/Archivos/File/File5146-inf-esta-18.pdf. Accessed 2012 Mar 6.

http://www.senasa.gov.ar/Archivos/File/File3851-File3851-mov-ganado-bovino-

[31] Arbolave F (2011) Costos Ganaderos 2011. Márgenes Agropecuarios. Suplemento

http://www.econoagro.com/downloads/act\_gan\_06\_09.pdf. Accessed 2011 Nov 23. [33] Márgenes Agropecuarios (2011) Relaciones Producto/Producto. Precio de la Hacienda.

http://www.ipcva.com.ar/documentos/1035\_informemensualdeexportacionesdiciembre

[35] USDA (2011) Livestock and Poultry: World Markets and Trade. Available: http://www.fas.usda.gov/psdonline/circulars/livestock\_poultry.pdf. Accessed 2011 Dic 7. [36] Guevara J C, Bertiller M B, Estevez O R, Grünwaldt E G, Allegretti, L I (2006) Pastizales y Producción Animal en las Zonas Áridas de Argentina. Sécheresse. 17 (1-2): 242-256. Available: http://www.jle.com/en/revues/agro\_biotech/sec/e-docs/00/04/1F/18/article.phtml [37] Guevara J C, Grünwaldt E G (2012) The Desert Environment of Mendoza, Argentina: Status and Prospects for Sustainable Beef Cattle Production. In: Guevara J C, Grünwaldt E G, Sivaperuman C, editors. Desert: Fauna, Flora and Environment. New York: Nova Science Publishers, Inc. pp. 115-127. Available: https://www.novapublishers.com/

http://www.siia.gov.ar/index.php/series-por-tema/ganaderia. Accessed 2012 May 14. [39] Dirección de Estadísticas e Investigaciones Económicas Mendoza (2011). Available: http://www.deie.mendoza.gov.ar/tematicas/menu\_tematicas.asp?filtro=Censo%20Nacio

[40] Guevara J C, Grünwaldt E G, Bifaretti A. (2010) Determinación de la rentabilidad de la recría de bovinos de carne en la provincia de Mendoza, Argentina. Revista de la Facultad de Ciencias Agrarias, Universidad Nacional de Cuyo. 42 (2): 23-37. Available: http://revista.fca.uncu.edu.ar/images/stories/pdfs/2010-02/T42\_2\_03\_Guevara.pdf [41] Grünwaldt E G, Guevara J C (2011) Rentabilidad del engorde a corral de bovinos de carne en la provincia de Mendoza, Argentina. Revista de la Facultad de Ciencias

http://bdigital.uncu.edu.ar/objetos\_digitales/4310/t43-2-02-grunwaldt-guevara.pdf [42] Tonelli V (2011) La Ganadería de los Próximos 4 Años. Márgenes Agropecuarios.

[43] Márgenes Agropecuarios (2011) Relaciones Insumo/Producto. Suplemento Ganadero:

Ruminant livestock systems contribute significantly to global anthropogenic methane emissions, with about 50% or more of the GHG emissions produced coming from enteric fermentation [1]. The loss of dietary energy in the form of methane has been extensively researched and reviewed [2, 3, 4]. Microorganisms called methanogens produce methane (methanogenesis) in the digestive tract as a by-product of anaerobic fermentation. Briefly, the process of methanogenesis [see 5, 6 for a more detailed summary] consists of:

1. Glucose equivalents from plant polymers or starch (cellulose, hemicellulose, pectin, starch, sucrose, fructans and pentosans) are hydrolysed by extracellular microbial enzymes to form pyruvate in the presence of protozoa and fungi in the digestive tract:

Glucose 2 pyruvate 4H → +

2. The fermentation of pyruvate involves oxidation reactions under anaerobic conditions producing reduced co-factors such as NADH. Reduced co-factors such as NADH are then re-oxidised to NAD to complete the synthesis of volatile fatty acids (VFAs) with the main products being acetate, butyrate and propionate (anions of acetic, butyric and propionic VFAs):

$$\begin{aligned} \text{Pyruvate + H}\_2\text{O} &\rightarrow \text{acetate (C2) + CO}\_2 + 2\text{H} \\ \text{2C2 + 4H} &\rightarrow \text{butylarate (C4) + 2H}\_2\text{O} \\ \text{Pyruvate + 4H} &\rightarrow \text{propylante (C3) + H}\_2\text{O} \end{aligned}$$

3. The VFAs are then available to be absorbed through the digestive mucosa into the animal's blood stream. The production of acetate and butyrate production provides a

net source of hydrogen or alternatively propionate can utilise any available hydrogen Methanogens eliminate the available hydrogen by using carbon dioxide (CO2) to produce methane:

Reducing Enteric Methane Losses

from Ruminant Livestock – Its Measurement, Prediction and the Influence of Diet 137

behaviour of the animal causing depression of appetite [14, 15], which may be avoided by making the walls of the enclosed environment transparent. A more mobile chamber that has been used is a polythene tunnel. Due to the polythene tunnel being mobile it is adaptable to different feeding systems such as grazing animals [14, 16]. However, difficulties in controlling the tunnel's temperature and humidity have been found, resulting in a lower

Chamber Open-circuit indirect respiration calorimeter. Air blown in and

Polythene tunnel Air blown in and extracted out of tunnel. Air concentrations

Room tracer gas Tracer gas is released into a ventilated room until a steady

Head box, hood or mask Respired gas volume can be sampled at regular intervals.

**Table 1.** A general summary of a few methods used to collect air samples to measure whole animal

In comparison to methods that use a controlled and enclosed environment, methods that use a tracer gas such as SF6 as a marker tend to be less costly and more applicable to use on a greater number of animals. The room tracer [17] and mass balance micrometerological methods, where a known amount of gas i.e. a tracer gas or the gas of interest are released from fixed points [18, 19, 20], both require careful monitoring of the sampling environment and diffusion of the gas of interest (in this case methane) needs to be tested prior to

Background air samples are required.

extracted out of a chamber. Air concentrations between the incoming and outgoing air are continuously monitored using gas analysers. Chamber conditions are controlled and monitored

between the incoming and outgoing air are continuously

concentration is reached, after which air samples can be collected.

Background air samples and a high precision gas analyser are

Typically using the inert sulphur hexafluoride (SF6) tracer gas. Assumes that the emitted tracer gas from a permeation tube in the rumen simulates the diffusion of any methane emitted. Respired air collected via a capillary tube near the animal's nostrils into a

required. Sampling downwind (and up) of the source.

estimate of methane production compared to chamber measurements [14, 16].

usually for 48 hours.

monitored.

vessel.

enteric methane emissions or solely eructated emissions

Method of measurement Description

**Whole animal emissions measured**

Mass balance micrometerological

**Eructated emissions measured**

ERUCT (Emissions from ruminants using a calibrated tracer)

$$\text{4H}\_2 + \text{CO}\_2 \rightarrow \text{CH}\_4 + 2\text{H}\_2\text{O}$$

In ruminants, some 87 to 93% of methane production occurs in the foregut, with the highest rate of production being after eating [7]. In sheep, almost 90% of the methane produced in the hindgut has been found to be absorbed and expired through the lungs, with the remainder being excreted through the rectum [8]. Rectum enteric methane losses have been estimated at 7% [9] and 8% [10] of methane output in dairy cows compared to the 1% found in sheep [8].

Reductions in enteric methane production from ruminants can result from a reduction in rumen fermentation rate (suppression in microbial activity) or a shift in VFA production [11]. An inverse relationship exists between the production of methane in the rumen and the presence of propionate. If the ratio of acetate to propionate was greater than 0.5, then hydrogen would become available to form methane [12]. If the hydrogen produced is not correctly used by methanogens, such as when large amounts of fermentable carbohydrate are fed, ethanol or lactate can form, which inhibits microbial growth, forage digestion, and any further production of VFAs [13]. In practice, ethanol or lactate may form, but any excess hydrogen is simply eructated.

The methods for sampling, measuring and predicting enteric methane production (using studies on dairy cattle as an example), and the influence of dietary components on methane production are reviewed.

## **2. Methods used to sample and measure methane production**

Estimates of methane output from livestock can be costly and difficult to make, especially from larger ruminants. Standard methods for measuring the methane concentration in air are by infrared spectroscopy, gas chromatography, mass spectroscopy or a tuneable laser diode. In a controlled and enclosed environment (i.e. chamber) the gas concentration can be calculated directly from the difference between ingoing and outgoing air, but in less contained environments a tracer gas is required as a marker, which is often the inert sulphur hexafluoride (SF6) gas.

Of the methods summarised [from the reviews of 7, 12] in Table 1 that can be used to sample air for its methane concentration, the open-circuit indirect respiration calorimeter (chamber) is acknowledged as currently providing the most reliable and repeatable method of obtaining an estimate of individual whole animal enteric methane emissions (including eructated and flatulence emissions) over a continuous sampling period [7]. If this method becomes less costly to implement, direct selection of animals on methane output could become possible. In some cases, there are suggestions that this technique may affect the behaviour of the animal causing depression of appetite [14, 15], which may be avoided by making the walls of the enclosed environment transparent. A more mobile chamber that has been used is a polythene tunnel. Due to the polythene tunnel being mobile it is adaptable to different feeding systems such as grazing animals [14, 16]. However, difficulties in controlling the tunnel's temperature and humidity have been found, resulting in a lower estimate of methane production compared to chamber measurements [14, 16].

136 Livestock Production

in sheep [8].

produce methane:

hydrogen is simply eructated.

production are reviewed.

hexafluoride (SF6) gas.

net source of hydrogen or alternatively propionate can utilise any available hydrogen Methanogens eliminate the available hydrogen by using carbon dioxide (CO2) to

+ →+ 2 2 42 4H CO CH 2H O

In ruminants, some 87 to 93% of methane production occurs in the foregut, with the highest rate of production being after eating [7]. In sheep, almost 90% of the methane produced in the hindgut has been found to be absorbed and expired through the lungs, with the remainder being excreted through the rectum [8]. Rectum enteric methane losses have been estimated at 7% [9] and 8% [10] of methane output in dairy cows compared to the 1% found

Reductions in enteric methane production from ruminants can result from a reduction in rumen fermentation rate (suppression in microbial activity) or a shift in VFA production [11]. An inverse relationship exists between the production of methane in the rumen and the presence of propionate. If the ratio of acetate to propionate was greater than 0.5, then hydrogen would become available to form methane [12]. If the hydrogen produced is not correctly used by methanogens, such as when large amounts of fermentable carbohydrate are fed, ethanol or lactate can form, which inhibits microbial growth, forage digestion, and any further production of VFAs [13]. In practice, ethanol or lactate may form, but any excess

The methods for sampling, measuring and predicting enteric methane production (using studies on dairy cattle as an example), and the influence of dietary components on methane

Estimates of methane output from livestock can be costly and difficult to make, especially from larger ruminants. Standard methods for measuring the methane concentration in air are by infrared spectroscopy, gas chromatography, mass spectroscopy or a tuneable laser diode. In a controlled and enclosed environment (i.e. chamber) the gas concentration can be calculated directly from the difference between ingoing and outgoing air, but in less contained environments a tracer gas is required as a marker, which is often the inert sulphur

Of the methods summarised [from the reviews of 7, 12] in Table 1 that can be used to sample air for its methane concentration, the open-circuit indirect respiration calorimeter (chamber) is acknowledged as currently providing the most reliable and repeatable method of obtaining an estimate of individual whole animal enteric methane emissions (including eructated and flatulence emissions) over a continuous sampling period [7]. If this method becomes less costly to implement, direct selection of animals on methane output could become possible. In some cases, there are suggestions that this technique may affect the

**2. Methods used to sample and measure methane production** 


**Table 1.** A general summary of a few methods used to collect air samples to measure whole animal enteric methane emissions or solely eructated emissions

In comparison to methods that use a controlled and enclosed environment, methods that use a tracer gas such as SF6 as a marker tend to be less costly and more applicable to use on a greater number of animals. The room tracer [17] and mass balance micrometerological methods, where a known amount of gas i.e. a tracer gas or the gas of interest are released from fixed points [18, 19, 20], both require careful monitoring of the sampling environment and diffusion of the gas of interest (in this case methane) needs to be tested prior to commencing sampling. The temperature, air pressure, humidity and air speed should also be monitored for their consistency in a non-enclosed sampling environment. Controlling the sampling environment would make replicating these techniques consistently on commercial farms difficult. Also, in some countries the use of SF6 is not permitted and there may be a withdrawal period on products from animals exposed to the gas [7]. The ERUCT (emissions from ruminants using a calibrated tracer) technique [9, 21] or a head box, hood or mask [22, 23] estimate eructated methane emissions from individual animals. This ignores enteric methane from the rectum, which could be 1 to 8% of total enteric methane production of an animal as previously discussed. The ERUCT technique was devised to allow measurement of methane emissions from free ranging and feedlot animals. The ERUCT technique has been found to be suitable for estimating respired methane emissions from high forage fed animals and not with animals on diets that result in greater post-ruminal digestion [21, 24]. Even though the ERUCT technique is more open to errors in estimates compared to using a chamber, these errors could be reduced by removal of outlying estimates and replicating sampling over several days [10]. More invasive methods of estimating methane production from rumen fluid involve injecting radioactively labelled methane (isotope dilution technique) [8, 25] or ethane [26] into the rumen.

Reducing Enteric Methane Losses

Sampling method

balance

balance

balance

micrometeorological mass

from Ruminant Livestock – Its Measurement, Prediction and the Influence of Diet 139

Methane (kg/hd/yr)

Body weight

[10] 18 496 120 ERUCT / Chamber [17] 25 - 102 Room tracer (SF6)

[18] - 600 142 Micrometeorological mass

[31] 18 602 137 Micrometeorological mass

[38] 12 526 84 Chamber / mask / ERUCT /

**Table 2.** Some of the key experiments globally that have measured methane output from dairy cattle\*

Prediction methods can be either empirical or mechanistic. Several reviews have studied the use and performance of different methane output prediction equations [11, 12, 33, 38, 45, 46,

Mechanistic equations estimate methane output using mathematical descriptions of rumen fermentation. Even though mechanistic equations at present appear to show the greatest degree of adaptability across diet types and intake level [48, 50, 51], they require detailed and complex dietary input values. Published mechanistic equations are not presented in this review but are described in [52] (recommended in [50] and [46]), [53], [54], [55], [56], [57],

Empirical equations such as those shown in Table 3 offer a more practical solution to predicting methane output using input variables such as digestibility, carbohydrate content, energy and nitrogen intake, milk production and live weight. Table 3 and Figure 1 present

(kg)

[27] 1 - 15 162 - 655 39 Chamber [28] 9 - 79 Chamber [29] - - 40 Chamber [30] 8 - 18 - 68 - 122 Chamber

[32] - 450 - 700 112 Chamber [33] 4 - 29 426 - 852 24 - 198 Chamber [34] 13 402 - 562 96 ERUCT [35] 13 517 95 Chamber [36] 14 - 16 595 138 Chamber [37] 14 - 109 ERUCT

[39] 20 572 137 Chamber [40] 8 - 25 379 - 733 72 - 210 Chamber [41] 2 - 29 173 - 826 13 - 197 Chamber \* Most recent reference to data collected is shown and values where available are presented.

**4. Methane output prediction equations** 

[58], [59] (recommended in [50]) and [60].

47, 48, 49].

Reference Dry matter intake (kg/day)

## **3. Methane output measurements**

Studies measuring the methane production of livestock have been carried out for over 80 years (Table 2). In the last 20 years the number of studies globally that have measured enteric methane have increased, as have the range of sampling methods used.

In cattle, the use of high energy dense diets has increased the amount of dry matter (DM) that an animal can consume, as a result of improved efficiencies in rumen fermentation and feed digestibility [42]. The level of intake of feed (more specifically organic matter) influences methane production. Dairy cows ranging in live weight from 385 to 747 kg were found to produce between 45 and 199 kg methane/head/yr (14 to 31 g/kg DM intake) of methane and beef cattle of 364 to 627 kg live weight produced between 40 and 92 kg methane/head/yr (13 to 35 g/kg DM intake), with the difference attributed to the amount of DM consumed [43]. Notably in Table 2 the highest DM intake measured was 29 kg/day in two of the studies [33, 41] and the methane production was also the same at 19 g/kg DM intake. Where a high energy dense diet is formulated to meet the nutrient requirements of a high milk yielding animal, it would appear that the methane output per kg DM intake could average about 19 g/kg, but this would be slightly more for high forage diets where potential intake is lower (0.21 g/kg DM or more [44]). As well as the influence of the composition of the diet, reductions in methane losses per kg DM intake appear to be possible by an incremental increase in the level of feed intake, brought about by increasing the proportion of concentrate feed in the diet. It has been suggested that this decrease in the percentage of dietary GE intake lost as methane occurs at an average of 1.6% per unit increase in feed level [12].

Reducing Enteric Methane Losses from Ruminant Livestock – Its Measurement, Prediction and the Influence of Diet 139


\* Most recent reference to data collected is shown and values where available are presented.

**Table 2.** Some of the key experiments globally that have measured methane output from dairy cattle\*

#### **4. Methane output prediction equations**

138 Livestock Production

rumen.

[12].

**3. Methane output measurements** 

commencing sampling. The temperature, air pressure, humidity and air speed should also be monitored for their consistency in a non-enclosed sampling environment. Controlling the sampling environment would make replicating these techniques consistently on commercial farms difficult. Also, in some countries the use of SF6 is not permitted and there may be a withdrawal period on products from animals exposed to the gas [7]. The ERUCT (emissions from ruminants using a calibrated tracer) technique [9, 21] or a head box, hood or mask [22, 23] estimate eructated methane emissions from individual animals. This ignores enteric methane from the rectum, which could be 1 to 8% of total enteric methane production of an animal as previously discussed. The ERUCT technique was devised to allow measurement of methane emissions from free ranging and feedlot animals. The ERUCT technique has been found to be suitable for estimating respired methane emissions from high forage fed animals and not with animals on diets that result in greater post-ruminal digestion [21, 24]. Even though the ERUCT technique is more open to errors in estimates compared to using a chamber, these errors could be reduced by removal of outlying estimates and replicating sampling over several days [10]. More invasive methods of estimating methane production from rumen fluid involve injecting radioactively labelled methane (isotope dilution technique) [8, 25] or ethane [26] into the

Studies measuring the methane production of livestock have been carried out for over 80 years (Table 2). In the last 20 years the number of studies globally that have measured

In cattle, the use of high energy dense diets has increased the amount of dry matter (DM) that an animal can consume, as a result of improved efficiencies in rumen fermentation and feed digestibility [42]. The level of intake of feed (more specifically organic matter) influences methane production. Dairy cows ranging in live weight from 385 to 747 kg were found to produce between 45 and 199 kg methane/head/yr (14 to 31 g/kg DM intake) of methane and beef cattle of 364 to 627 kg live weight produced between 40 and 92 kg methane/head/yr (13 to 35 g/kg DM intake), with the difference attributed to the amount of DM consumed [43]. Notably in Table 2 the highest DM intake measured was 29 kg/day in two of the studies [33, 41] and the methane production was also the same at 19 g/kg DM intake. Where a high energy dense diet is formulated to meet the nutrient requirements of a high milk yielding animal, it would appear that the methane output per kg DM intake could average about 19 g/kg, but this would be slightly more for high forage diets where potential intake is lower (0.21 g/kg DM or more [44]). As well as the influence of the composition of the diet, reductions in methane losses per kg DM intake appear to be possible by an incremental increase in the level of feed intake, brought about by increasing the proportion of concentrate feed in the diet. It has been suggested that this decrease in the percentage of dietary GE intake lost as methane occurs at an average of 1.6% per unit increase in feed level

enteric methane have increased, as have the range of sampling methods used.

Prediction methods can be either empirical or mechanistic. Several reviews have studied the use and performance of different methane output prediction equations [11, 12, 33, 38, 45, 46, 47, 48, 49].

Mechanistic equations estimate methane output using mathematical descriptions of rumen fermentation. Even though mechanistic equations at present appear to show the greatest degree of adaptability across diet types and intake level [48, 50, 51], they require detailed and complex dietary input values. Published mechanistic equations are not presented in this review but are described in [52] (recommended in [50] and [46]), [53], [54], [55], [56], [57], [58], [59] (recommended in [50]) and [60].

Empirical equations such as those shown in Table 3 offer a more practical solution to predicting methane output using input variables such as digestibility, carbohydrate content, energy and nitrogen intake, milk production and live weight. Table 3 and Figure 1 present empirical prediction equations for methane output developed using animals that included dairy cattle, with a range of intakes and different diets. Of the empirical prediction equations shown in Table 3, studies have compared the predictions of an equation against methane measurements, with some being recommended such as [29] (recommended in [33]), [61] (recommended in [33], [12], [46] and [47]), [62] (recommended in [63]) and the non-linear equations using DM intake and metabolisable energy (ME) intake by [47] (recommended in [48] and [38]).

Reducing Enteric Methane Losses

from Ruminant Livestock – Its Measurement, Prediction and the Influence of Diet 141

Reference Units Equation

[66] g/day = 41 + 30 × DS + 6 × S + 51 × DCW

[69] L/day = 47.82 × DMI - 0.762 × DMI2 - 41

L/day = 0.666 × LWGT + 2.868 × MY + 75

proportion (kg/kg DM); N = nitrogen (kg/day); S = starch (kg/day).

inputs and production values for dairy cattle

[68] L/day = 38.92 + 26.44 × DMI

[67] MJ/day = 1.36 + 1.21 × DMI - 0.825 × CDMI + 12.8 × NDF

 L/day = 39.2 × DMI - 0.588 × DMI2 + 0.370 × LWGT - 1.698 × MY – 134 DMI = dry matter intake (kg/day); CDMI = concentrate DMI (kg/day); FDMI = forage DMI (kg/day); TC = total NDF, sugar and starch (100 g/day); D = digestibility of gross energy at maintenance (%); NFC = non-fibre carbohydrate (kg/day); HC = hemicellulose (kg/day); C = cellulose (kg/day); MY = milk yield (kg/day); MF = milk fat composition (%); MP = milk protein composition (%); CP = crude protein (% DMI); F = fat (% DMI); DMD = DM digestibility (%); CPD = CP digestibility (%); ADFD = acid detergent fibre digestibility (%); NDSD = neutral detergent solubles digestibility (%); CD = cellulose digestibility (%); HD = hemicellulose digestibility (%); DS = sugars (kg/day); DCW = digested cell walls (kg/day); L = lignin (kg/day); LWGT = live weight (kg); DEI = digestible energy intake (MJ/day); MEI = metabolisable energy intake (MJ/day); GEI = gross energy intake (MJ/day); FADF = forage ADF (kg/day); TADF = total ADF (kg/day); FL = multiples of MEI over maintenance; NDF = neutral detergent fibre (kg/kg DM); FP = forage

**Table 3.** Empirical equations from the literature that predict enteric methane output from dietary

considerable variation in methane output for a given level of DM intake [71].

In addition to dynamic and statistical prediction methods, methane output can be estimated based on an animal's predicted energy requirements, which is the technique used in the Intergovernmental Panel on Climate Change (IPCC) methodology [72, 73]. This energy balance approach is suitable as an estimate over a period of time (as used in national inventories based on IPCC methodology) such as a year or lactation [74]. The IPCC methodology is based on production variables that are generally more easily obtained than

those used in empirical or even more dynamic enteric methane prediction equations.

The success or suitability of an empirical prediction equation for implementation on a data set is dependent on the range of values that the equation was developed on. A comparison of empirical prediction equations from Table 3, which were tested over a range of DM intakes from 1 to 35 kg/d (beyond the range they would have been developed on) for lactating dairy cows fed diets with a high and low proportion of forage content, suggest that the relationship between methane output and intake may be linear up to an average intake of 15 kg DM/d. Above this level of intake, which is more achievable by feeding a higher proportion of concentrates in the diet, the majority of equations showed a decline in methane output per unit intake (due to the increase in the level of intake by feeding a higher proportion of concentrate feed as has been suggested [12]; Fig. 1). This depression in methane lost per kg DM intake at high levels of intake in cattle has also been shown in other studies (reported in [71]). The main difference amongst the performances of methane prediction equations is their ability to give a sensible estimate of methane losses at low (approaching the origin) and high dry matter intakes. Even though some of the variation in the predictive ability of an equation in Figure 1 may be explained by the equation being used on a range of values outside the range it was developed on and the complexity of an equation, there is still

[70] L/day = 38.2 + 4.89 × FP × DMI - 0.719 × DMI2 – 20



(recommended in [48] and [38]).

Reference Units Equation

[27] g/day = 18 + 22.5 × DMI

[37] g/day = 17.1 × DMI + 97.4

[38] MJ/day = 8.56 + 0.14 × FP MJ/day = 3.23 + 0.81 × DMI

[28] MJ/day = -2.07 + 2.63 × DMI - 0.105 × DMI2

[32] g/day = 10.0 + 4.9 × MY + 1.5 × LWGT0.75

g/day = 91 + 50 × C + 40 × HC + 24 × S + 67 × DS

g/day = 84 + 47 × C + 32 × S + 62 × DS

[41] MJ/day = 74.43 - (74.43 + 0) × e[−0.0163 × DMI]

MJ/day = (7.16 - 0.101 × DMI)/100 × GEI

 MJ/day = 1.06 + 10.27 × FP + 0.87 × DMI MJ/day = 56.27 - (56.27 + 0) × e[−0.028 × DMI] MJ/day = 45.89 - (45.89 + 0) × e[−0.003 × MEI]

ADFD

MJ/day = 7.30 + 13.13 × N + 2.04 TADF + 0.33 × S

[61] MJ/day = 3.38 + 0.51 × NFC + 1.74 × HC + 2.652 × C

DMD - 0.0737 × CPD

 MJ/day = 2.6861 + 0.0779 × DEI [47] MJ/day = 5.93 + 0.92 × DMI MJ/day = 8.25 + 0.07 × MEI

[64] g/day = 4.012 × TC + 17.68

[29] MJ/day = [1.3 + 0.112 × D + FL × (2.37 - 0.05 × D)/100] × GEI

g/day = 123 + 84 × C - 30 × HC + 58 × S + 73 × DS - 95 × L

3.1003)]/0.6127 × DMI

MJ/day = 74.43 - (74.43 + 0) × e[cx]; cx = -0.0187 + 0.0059 / [1 + exp (S/TADF -

MJ/day = 45.98 - (45.98 + 0) × e[cx]; cx = -0.0011 × (S/TADF) + 0.0045 × MEI

[65] % GEI = 2.898 - 0.0631 × MY + 0.297 × MF - 1.587 × MP + 0.0891 × CP +

% GEI = 2.927 - 0.0405 × MY + 0.335 × MF - 1.225 × MP + 0.248 × CP - 0.448

% GEI = 227.099 - 2.783 × [(ADFD/DMI) × 100] - 6.0176 × ADFD + 3.607 ×

CPD + 1.751 × NDSD - 1.423 × CD + 1.203 × HD

0.1010 × [(FADF/DMI) × 100] + 0.l02 × DMI - 0.131 × F + 0.116 ×

× [(ADF/DMI) × 100] + 0.502 × [(FADF/DMI) × 100) + 0.0352 ×

[62] MJ/day = DEI × [0.094 + 0.028 × (FADF/TADF)] - 2.453 × (FL-1) MJ/day = DEI × [0.096 + 0.035 × (FDMI/DMI)] - 2.298 × (FL-1)

empirical prediction equations for methane output developed using animals that included dairy cattle, with a range of intakes and different diets. Of the empirical prediction equations shown in Table 3, studies have compared the predictions of an equation against methane measurements, with some being recommended such as [29] (recommended in [33]), [61] (recommended in [33], [12], [46] and [47]), [62] (recommended in [63]) and the non-linear equations using DM intake and metabolisable energy (ME) intake by [47]

> DMI = dry matter intake (kg/day); CDMI = concentrate DMI (kg/day); FDMI = forage DMI (kg/day); TC = total NDF, sugar and starch (100 g/day); D = digestibility of gross energy at maintenance (%); NFC = non-fibre carbohydrate (kg/day); HC = hemicellulose (kg/day); C = cellulose (kg/day); MY = milk yield (kg/day); MF = milk fat composition (%); MP = milk protein composition (%); CP = crude protein (% DMI); F = fat (% DMI); DMD = DM digestibility (%); CPD = CP digestibility (%); ADFD = acid detergent fibre digestibility (%); NDSD = neutral detergent solubles digestibility (%); CD = cellulose digestibility (%); HD = hemicellulose digestibility (%); DS = sugars (kg/day); DCW = digested cell walls (kg/day); L = lignin (kg/day); LWGT = live weight (kg); DEI = digestible energy intake (MJ/day); MEI = metabolisable energy intake (MJ/day); GEI = gross energy intake (MJ/day); FADF = forage ADF (kg/day); TADF = total ADF (kg/day); FL = multiples of MEI over maintenance; NDF = neutral detergent fibre (kg/kg DM); FP = forage proportion (kg/kg DM); N = nitrogen (kg/day); S = starch (kg/day).

**Table 3.** Empirical equations from the literature that predict enteric methane output from dietary inputs and production values for dairy cattle

The success or suitability of an empirical prediction equation for implementation on a data set is dependent on the range of values that the equation was developed on. A comparison of empirical prediction equations from Table 3, which were tested over a range of DM intakes from 1 to 35 kg/d (beyond the range they would have been developed on) for lactating dairy cows fed diets with a high and low proportion of forage content, suggest that the relationship between methane output and intake may be linear up to an average intake of 15 kg DM/d. Above this level of intake, which is more achievable by feeding a higher proportion of concentrates in the diet, the majority of equations showed a decline in methane output per unit intake (due to the increase in the level of intake by feeding a higher proportion of concentrate feed as has been suggested [12]; Fig. 1). This depression in methane lost per kg DM intake at high levels of intake in cattle has also been shown in other studies (reported in [71]). The main difference amongst the performances of methane prediction equations is their ability to give a sensible estimate of methane losses at low (approaching the origin) and high dry matter intakes. Even though some of the variation in the predictive ability of an equation in Figure 1 may be explained by the equation being used on a range of values outside the range it was developed on and the complexity of an equation, there is still considerable variation in methane output for a given level of DM intake [71].

In addition to dynamic and statistical prediction methods, methane output can be estimated based on an animal's predicted energy requirements, which is the technique used in the Intergovernmental Panel on Climate Change (IPCC) methodology [72, 73]. This energy balance approach is suitable as an estimate over a period of time (as used in national inventories based on IPCC methodology) such as a year or lactation [74]. The IPCC methodology is based on production variables that are generally more easily obtained than those used in empirical or even more dynamic enteric methane prediction equations.

Reducing Enteric Methane Losses

from Ruminant Livestock – Its Measurement, Prediction and the Influence of Diet 143

common inputs to equations in Table 3. However, this would suggest that high starch feeds such as cereal grain would increase methane emissions. But when fed at an increasing level of intake cereal grains have a curvilinear effect on fibre digestion in mixed rations ([71]; expressed as a ratio of starch to acid detergent fibre content in [41, 47]) and result in a

Diet composition can influence rumen fermentation and reduce methane production as a result of more propionate present or less degradation of food consumed in the rumen. Postruminal digestion, particularly in the small intestine, is energetically more efficient with lower methane losses than digestion in the rumen, which can be encouraged by more digestible and higher quality food. The amount and type of dietary carbohydrate fermented affects the fermentation rate and rumen retention time of substrate, in addition to the hydrogen supply due to the ratio of acetate to propionate. The passage rate of substrate and rumen fluid dilution rate (influencing the ratio of acetate to propionate) have been found to explain 28% and 25% of variation in an animal's methane production [77]. Cellulose ferments more slowly than hemicellulose, but both these structural carbohydrates ferment more slowly than non-structural and more soluble carbohydrates such as starch and sugars [2]. With regard to forages, increasing the digestibility of forage fed by reducing fibre content can reduce methane production. Feeding maize silage [78] or a legume-based silage [45] rather than grass silage has been found to reduce methane production. Also, silage is generally more digestible than hay [45] and adding molasses or urea to straw made it more digestible [79], which in both cases reduced methane production. Forage methane production can be minimised by lower fibre content and high soluble carbohydrate (influenced by maturity), and C3 grasses rather than C4 [2]. The grinding or pelleting of forage to increase its surface area

The additions of feed additives to a ruminant's diet have been and are still being extensively evaluated for their effect on reducing methane emissions. The benefit in animal productivity and reduction in methane production relative to the cost of using different additives is continually being assessed. As previously suggested, the supplementation of diets with additives such as fats can reduce methane production [12, 44, 65, 81, 82, 83, 84] particularly fats with C8 to C16 chain length such as coconut oil [56, 85], however the effect, which is a suppression on fermentation appears to not always last [17, 37]. Suppressing fermentation by supplementing the diet with fat inhibits methanogens and protozoa, and subsequent fibre digestion with a shift towards more propionate present rather than acetate [2]. Likewise, the use of ionophores in feed (particularly monensin and salinomycin) and spices [86] that modify the rumen microflora [87] can reduce methane losses [6, 7, 88, 89] and encourage a shift towards propionogenesis. However eventually the rumen microflora would appear to show some resistance and the suppression ceases [90, 91, 92]. The inconsistent effects of monensin on methane in dairy cattle on forage and grain supplemented diets have also been found [93, 94]. Notably, ionophores are banned within

Other feed additives tested include the use of plant compounds such as tannins (inhibiting methanogens) [95] and saponins (inhibiting protozoa), which reduce the digestibility of

depression in methane per unit DM (as in Fig.1 in [47]) and per unit product.

and digestibility could also help reduce methane production [12, 80].

the European Union due to the fears of residues appearing in the milk.

**Figure 1.** Average methane output polynomial trend lines for methane output predictions by published equations (in Table 3) x[1] to x[25] across a range of daily dry matter intakes of dairy cows (from [49])

#### **5. Effect of diet on methane output**

As suggested in Figure 1 and proposed by others [29], increased intake of less digestible feeds such as forage has little effect on methane production per DM intake, whereas an increase in more digestible feeds such as concentrate results in a reduction in methane losses per DM intake. This improvement in the quality of food fed to a ruminant is an effective way to manipulate the diet (particularly in terms of digestible organic matter) to get better animal performance and reduced methane production [40, 45, 70, 75].

Individual feeds can vary considerably in their methanogenic effect based on their chemical composition. An evaluation of chamber measurements of methane from sheep fed different feeds found a range for percentage of GE lost as methane from 3.8% for distillers grains to 12.8% for peas [76]. The authors found that 92% of the variation in methane emission was explained by the equation:

Methane output (% GE) = -10.5 + 0.192 × DE – 0.0567 × EE + 0.00651 × S + 0.00647 × CP + 0.0111 × NDF

where, DE is digestible energy (% of gross energy, GE), EE is ether extract, S is starch, CP is crude protein and NDF is neutral detergent fibre (all in g/kg DM).

The above equation shows the relative response in methane output to each dietary component, with increases in DE, S, CP increasing methane emissions and increasing EE reducing methane. These parameters and their positive or negative effect on methane are common inputs to equations in Table 3. However, this would suggest that high starch feeds such as cereal grain would increase methane emissions. But when fed at an increasing level of intake cereal grains have a curvilinear effect on fibre digestion in mixed rations ([71]; expressed as a ratio of starch to acid detergent fibre content in [41, 47]) and result in a depression in methane per unit DM (as in Fig.1 in [47]) and per unit product.

142 Livestock Production

**Figure 1.** Average methane output polynomial trend lines for methane output predictions by published equations (in Table 3) x[1] to x[25] across a range of daily dry matter intakes of dairy cows (from [49])

As suggested in Figure 1 and proposed by others [29], increased intake of less digestible feeds such as forage has little effect on methane production per DM intake, whereas an increase in more digestible feeds such as concentrate results in a reduction in methane losses per DM intake. This improvement in the quality of food fed to a ruminant is an effective way to manipulate the diet (particularly in terms of digestible organic matter) to get better

Individual feeds can vary considerably in their methanogenic effect based on their chemical composition. An evaluation of chamber measurements of methane from sheep fed different feeds found a range for percentage of GE lost as methane from 3.8% for distillers grains to 12.8% for peas [76]. The authors found that 92% of the variation in methane emission was

Methane output (% GE) = -10.5 + 0.192 × DE – 0.0567 × EE + 0.00651 × S + 0.00647 × CP +

where, DE is digestible energy (% of gross energy, GE), EE is ether extract, S is starch, CP is

The above equation shows the relative response in methane output to each dietary component, with increases in DE, S, CP increasing methane emissions and increasing EE reducing methane. These parameters and their positive or negative effect on methane are

animal performance and reduced methane production [40, 45, 70, 75].

crude protein and NDF is neutral detergent fibre (all in g/kg DM).

**5. Effect of diet on methane output** 

explained by the equation:

0.0111 × NDF

Diet composition can influence rumen fermentation and reduce methane production as a result of more propionate present or less degradation of food consumed in the rumen. Postruminal digestion, particularly in the small intestine, is energetically more efficient with lower methane losses than digestion in the rumen, which can be encouraged by more digestible and higher quality food. The amount and type of dietary carbohydrate fermented affects the fermentation rate and rumen retention time of substrate, in addition to the hydrogen supply due to the ratio of acetate to propionate. The passage rate of substrate and rumen fluid dilution rate (influencing the ratio of acetate to propionate) have been found to explain 28% and 25% of variation in an animal's methane production [77]. Cellulose ferments more slowly than hemicellulose, but both these structural carbohydrates ferment more slowly than non-structural and more soluble carbohydrates such as starch and sugars [2]. With regard to forages, increasing the digestibility of forage fed by reducing fibre content can reduce methane production. Feeding maize silage [78] or a legume-based silage [45] rather than grass silage has been found to reduce methane production. Also, silage is generally more digestible than hay [45] and adding molasses or urea to straw made it more digestible [79], which in both cases reduced methane production. Forage methane production can be minimised by lower fibre content and high soluble carbohydrate (influenced by maturity), and C3 grasses rather than C4 [2]. The grinding or pelleting of forage to increase its surface area and digestibility could also help reduce methane production [12, 80].

The additions of feed additives to a ruminant's diet have been and are still being extensively evaluated for their effect on reducing methane emissions. The benefit in animal productivity and reduction in methane production relative to the cost of using different additives is continually being assessed. As previously suggested, the supplementation of diets with additives such as fats can reduce methane production [12, 44, 65, 81, 82, 83, 84] particularly fats with C8 to C16 chain length such as coconut oil [56, 85], however the effect, which is a suppression on fermentation appears to not always last [17, 37]. Suppressing fermentation by supplementing the diet with fat inhibits methanogens and protozoa, and subsequent fibre digestion with a shift towards more propionate present rather than acetate [2]. Likewise, the use of ionophores in feed (particularly monensin and salinomycin) and spices [86] that modify the rumen microflora [87] can reduce methane losses [6, 7, 88, 89] and encourage a shift towards propionogenesis. However eventually the rumen microflora would appear to show some resistance and the suppression ceases [90, 91, 92]. The inconsistent effects of monensin on methane in dairy cattle on forage and grain supplemented diets have also been found [93, 94]. Notably, ionophores are banned within the European Union due to the fears of residues appearing in the milk.

Other feed additives tested include the use of plant compounds such as tannins (inhibiting methanogens) [95] and saponins (inhibiting protozoa), which reduce the digestibility of dietary fibre [96], and organic acids such as fumarate, malate and acrylate which act as an alternative hydrogen acceptor [97], but results for effects on methane production and animal performance are variable [3]. Probiotics (acetogens and yeast) have been found to reduce methane output, mainly through improving digestion efficiency [88] but not by others [3]. Overall, unless yeast by-products can reliably be used to reduce methane production, the most cost-effective additive for reducing production appears to be the addition of cellulase and hemicellulase enzymes to a ruminant's diet, which not only improved fibre digestion but also productivity [98].

Reducing Enteric Methane Losses

from Ruminant Livestock – Its Measurement, Prediction and the Influence of Diet 145

[1] Steinfeld H, Gerber P, Wassenaar T, Castel V, Rosales M, de Haan C (2006) Livestock's

[2] Eckard R.J, Grainger C, de Klein C.A.M. (2010) Options for the abatement of methane and nitrous oxide from ruminant production: A review. Livest. Sci. 130:47-56. [3] Martin C, Morgavi D.P, Doreau M (2010) Methane mitigation in ruminants: from

[4] Cottle D.J, Nolan J.V, Wiedemann SG (2011) Ruminant enteric methane mitigation: a

[5] McDonald P, Edwards R.A, Greenhalgh J.F.D, Morgan C.A (1995) Animal Nutrition.

[6] Moss A.R, Jouany J-P, Newbold J (2000) Methane production by ruminants: its

[7] Kebreab E, Clark K, Wagner-Riddle C, France J (2006a) Methane and nitrous oxide emissions from Canadian animal agriculture: A review. Can. J. Anim. Sci. 86:135-158. [8] Murray R.M, Bryant A.M, Leng R.A (1976) Rates of production of methane in the rumen

[9] Grainger C, Clarke T, McGinn S.M, Auldist M.J, Beauchemin K.A, Hannah M.C, Waghorn G.C, Clark H, Eckard R.J (2007) Methane emissions from dairy cows measured using the sulphur hexafluoride (SF6) tracer and chamber techniques. J. Dairy

[10] Tamminga S, Bannink A, Dijkstra J, Zom R (2007) Feeding strategies to reduce methane

[11] Johnson K.A, Johnson D.E (1995) Methane emissions from cattle. J. Anim. Sci. 73:2483-2492. [12] Joblin K.N (1999) Ruminal acetogens and their potential to lower ruminant methane

[13] Murray P.J, Moss A, Lockyer D.R, Jarvis S.C (1999) A comparison of systems for

[14] Sherlock J (2005) Defra research in agriculture and environmental protection between 1990 and 2005: summary and analysis report (ES0127). Final report to Defra. Defra,

[15] Lockyer D.R, Jarvis S.C (1995) The measurement of methane losses from grazing

[16] Johnson K.A, Kincaid R.L, Westberg H.H, Gaskins C.T, Lamb B.K, Cronrath J.D (2002) The effect of oilseeds in diets of lactating cows on milk production and methane

[17] Kaharabata S.K, Schuepp P.H, Desjardins R (2000) Estimating methane emissions from dairy cattle housed in a barn and feedlot using an atmospheric tracer. Environ. Sci.

[18] Laubach J, Kelliher F (2005) Methane emissions from dairy cows: Comparing open-path laser measurements to profile-based techniques. Agricult. Forest Meteorol. 135:340-345. [19] Griffith D.W.T, Glenn R, Bryant D.H, Reisinger A.R (2008) Methane emissions from free-ranging cattle: comparison of tracer and integrated horizontal flux techniques. J.

loss in cattle. Animal Science Group report, Wageningen, The Netherlands.

measuring methane emissions from sheep. J. Agric. Sci. 133:439-444.

long shadow - Environmental issues and options. FAO report, Rome, Italy.

microbe to the farm scale. Animal 4:351-365.

Fifth Edition. Longman press, Harlow, UK.

and large intestines of sheep. Br. J. Nutr. 36:1-14.

emissions. Aust. J. Agric. Res. 50:1307-1313.

animals. Environ. Pollut. 90:383-390.

emissions. J. Dairy Sci. 85:1509-1515.

Technol. 34:3296-3302.

Environ. Qual. 37:582-591.

contribution to global warming. Ann. Zootech. 49:231-253.

review. Anim. Prod. Sci. 51:491-514.

Sci. 90:2755-2766.

London, UK.

**7. References** 

## **6. Conclusions**

With the increased importance now attached to enteric methane emissions from ruminants, due its global warming potential, there has been and will continue to be improvements in our understanding of methanogenesis and abatement options. Chamber measurements are costly in comparison to other measurement techniques and prediction methods, and therefore methane predictions using mechanistic models describing rumen fermentation are recognised at present as being more applicable to different feeds and animal species. The methane output from different feeds and animals has been extensively measured, predicted and tested but a robust empirical prediction of enteric methane emissions that can be applied to any ruminant production system is still to be developed. This is partly due to the need for the effect of feeding level to be better defined.

The important variables for predicting enteric methane output are the contents of fermentable carbohydrate, fibre, fat, digestible energy and intake level of a diet. Low enteric methane losses per unit DM appear possible by mechanisms that promote the passage of organic matter to post-rumen digestion and reduce rumen fermentation by high intakes of digestible feed and addition of fats, whilst also reducing emissions per unit product.

## **Author details**

M. J. Bell\* *Melbourne School of Land and Environment, University of Melbourne, Vic. 3010, Australia* 

R. J. Eckard

*Primary Industries Climate Challenges Centre, The University of Melbourne & Department of Primary Industries, Australia* 

## **Acknowledgement**

This work was supported by funding from Dairy Australia, Meat and Livestock Australia and the Australian Government Department of Agriculture, Fisheries and Forestry under its Australia's Farming Future Climate Change Research Program.

<sup>\*</sup> Corresponding Author

#### **7. References**

144 Livestock Production

but also productivity [98].

need for the effect of feeding level to be better defined.

**6. Conclusions** 

unit product.

M. J. Bell\*

R. J. Eckard

 \*

**Author details** 

*Primary Industries, Australia* 

**Acknowledgement** 

Corresponding Author

dietary fibre [96], and organic acids such as fumarate, malate and acrylate which act as an alternative hydrogen acceptor [97], but results for effects on methane production and animal performance are variable [3]. Probiotics (acetogens and yeast) have been found to reduce methane output, mainly through improving digestion efficiency [88] but not by others [3]. Overall, unless yeast by-products can reliably be used to reduce methane production, the most cost-effective additive for reducing production appears to be the addition of cellulase and hemicellulase enzymes to a ruminant's diet, which not only improved fibre digestion

With the increased importance now attached to enteric methane emissions from ruminants, due its global warming potential, there has been and will continue to be improvements in our understanding of methanogenesis and abatement options. Chamber measurements are costly in comparison to other measurement techniques and prediction methods, and therefore methane predictions using mechanistic models describing rumen fermentation are recognised at present as being more applicable to different feeds and animal species. The methane output from different feeds and animals has been extensively measured, predicted and tested but a robust empirical prediction of enteric methane emissions that can be applied to any ruminant production system is still to be developed. This is partly due to the

The important variables for predicting enteric methane output are the contents of fermentable carbohydrate, fibre, fat, digestible energy and intake level of a diet. Low enteric methane losses per unit DM appear possible by mechanisms that promote the passage of organic matter to post-rumen digestion and reduce rumen fermentation by high intakes of digestible feed and addition of fats, whilst also reducing emissions per

*Melbourne School of Land and Environment, University of Melbourne, Vic. 3010, Australia* 

*Primary Industries Climate Challenges Centre, The University of Melbourne & Department of* 

Australia's Farming Future Climate Change Research Program.

This work was supported by funding from Dairy Australia, Meat and Livestock Australia and the Australian Government Department of Agriculture, Fisheries and Forestry under its


[20] Vlaming J.B, Clark H, Lopez-Villalobos N (2005) The effect of SF6 release rate, animal species and feeding conditions on estimates of methane emissions from ruminants. In: Proceedings of the New Zealand Society for Animal Production, 65:4-8.

Reducing Enteric Methane Losses

from Ruminant Livestock – Its Measurement, Prediction and the Influence of Diet 147

[37] Ellis J.L, Kebreab E, Odongo N.E, McBride B.W, Okine E.K, France J (2007) Prediction of

[38] van Knegsel A.T.M, van den Brand H, Dijkstra J, van Straalen W.M, Heetkamp M.J.W, Tamminga S, Kemp B (2007) Dietary energy source in dairy cows in early lactation:

[39] Yan T, Mayne C.S, Gordon F.G, Porter M.G, Agnew R.E, Patterson D.C, Ferris C.P, Kilpatrick D.J (2010) Mitigation of enteric methane emissions through improving efficiency of energy utilization and productivity in lactating dairy cows. J. Dairy Sci.

[40] Mills J.A.N, Crompton L.A, Bannink A, Tamminga S, Moorby J, Reynolds C.K. (2009) Predicting methane emissions and nitrogen excretion from cattle. J. Agric. Sci. 147:741. [41] Eastridge M.L (2006) Major advances in applied dairy cattle nutrition. J Dairy Sci.

[42] Yan T, Porter M.G, Mayne C.S (2009) Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters. Animal 3:1455-

[43] Moate P.J, Williams S.R.O, Grainger C, Hannah M.C, Ponnampalam E.N, Eckard R.J (2011) Influence of cold-pressed canola, brewers grains and hominy meal as dietary supplements suitable for reducing enteric methane emissions from lactating dairy cows.

[44] Benchaar C, Pomar C, Chiquette J (2001) Evaluation of dietary strategies to reduce methane production in ruminants: a modelling approach. Can. J. Anim. Sci. 81:563-574. [45] Palliser C.C, Woodward S.L (2002) Using models to predict methane reduction in pasturefed dairy cows. In: Proceedings Integrating Management and Decision Support. Part 1, 482. (Coordinated by Susan M. Cuddy) (CSIRO, Australia) pp. 162-167. (CSIRO:

[46] Mills J.A.N, Kebreab E, Yates C.M, Crompton L.A, Cammell S.B, Dhanoa M.S, Agnew R.E, France J (2003) Alternative approaches to predicting methane emissions from dairy

[47] Kebreab E, France J, McBride B.W, Odongo N, Bannink A, Mills J.A.N, Dijkstra J (2006b) Evaluation of models to predict methane emissions from enteric fermentation in North American dairy cattle. In: Kebreab E, Dijkstra J, France J, Bannink A, Gerrits W.J.J (Eds.), Nutrient Digestion and Utilization in Farm Animals: Modelling Approaches.

[48] Bell M.J, Wall E, Russell G, Simm G (2009) Modelling methane output from lactating and dry dairy cows. In: MacLeod M, Mayne S, McRoberts N, Oldham J, Renwick A, Rivington M, Russell G, Toma L, Topp K, Wall E, Wreford A (Eds.), Aspects of Applied Biology 93, Integrated Agricultural Systems: Methodologies, Modeling and Measuring.

[49] Benchaar C, Rivest J, Pomar C, Chiquette J (1998) Prediction of methane production from dairy cows using existing mechanistic models and regression equations. J. Anim.

[50] Thornley J.H.M, France, J (2007) Mathematical Models in Agriculture. Second Edition.

methane production from dairy and beef cattle. J. Dairy Sci. 90:3456-3467.

energy partitioning and milk composition. J. Dairy Sci. 90:1467-1476.

93:2630-2638.

89:1311-1323.

Anim. Feed Sci. Technol. 166-167: 254-264.

cows. J. Anim. Sci. 81:3141-3150.

CAB International, Wallingford, UK.

CAB International, Wallingford, UK, pp. 299 -313.

Association of Applied Biologists, Wellesbourne, UK, pp. 47-53.

1462.

Canberra).

Sci. 76:617-627.


[37] Ellis J.L, Kebreab E, Odongo N.E, McBride B.W, Okine E.K, France J (2007) Prediction of methane production from dairy and beef cattle. J. Dairy Sci. 90:3456-3467.

146 Livestock Production

[20] Vlaming J.B, Clark H, Lopez-Villalobos N (2005) The effect of SF6 release rate, animal species and feeding conditions on estimates of methane emissions from ruminants. In:

[21] Belyea R.L, Marin P.J, Sedgwick H.T (1985) Utilization of chopped and long alfalfa by

[22] Kelly J.M, Kerrigan B, Milligan L.P, McBride B.W (1994) Development of a mobile, open

[23] McGinn S.M, Beauchemin K.A, Iwaasa A.D, McAllister T.A (2006) Assessment of the sulfur hexafluoride (SF6) tracer technique for measuring enteric methane emissions

[24] France J, Beever D.E, Siddons R.C (1993) Compartmental schemes for estimating methanogenesis in ruminants from isotope dilution data. J. Theor. Biol. 164:207-218. [25] Moate P.J, Clarke T, Davies L.H, Laby R.H (1997) Rumen gases and load in grazing

[26] Kriss M (1930) Quantitative relations of the dry matter of the food consumed, the heat production, the gaseous outgo, and the insensible loss in body weight of cattle. J. Agric.

[27] Axelsson J (1949) The amount of produced methane energy in the European metabolic experiments with adult cattle. Annals of the Royal Agricultural College of Sweden,

[28] Blaxter K.L, Clapperton J.L (1965) Prediction of the amount of methane produced by

[29] Shibata M, Terada F, Kurihara M, Nishida T, Iwasaki K (1993) Estimation of methane

[30] Kinsman R, Sauer F.D, Jackson H.A, Wolynetz M.S (1995) Methane and carbon dioxide emissions from dairy cows in full lactation monitored over a six-month period. J. Dairy

[31] Kirchgessner M, Windisch W, Muller H.L (1995) Nutritional factors for the quantification of methane production. In: Proceedings 8th International Symposium on Ruminant Physiology, Ruminant Physiology: Digestion, Metabolism Growth and

[32] Wilkerson V.A, Casper D.P, Mertens D.R (1995) The Prediction of methane production

[33] Ulyatt M.J, Lassey K.R, Martin R.J, Walker C.F, Shelton I.D (1997) Methane emission from grazing sheep and cattle. In: Proceedings of the New Zealand Society of Animal

[34] Bruinenberg M.H, van Der Honing Y, Agnew R.E, Yan T, van Vuuren A.M, Valk H (2002) Energy metabolism of dairy cows fed on grass. Livest. Prod. Sci. 75:117-128. [35] Hindrichsen I.K, Wettstein H-R, Machmuller A, Jorg B, Kreuzer M (2005) Effect of the carbohydrate composition of feed concentratates on methane emission from dairy cows

[36] Woodward S.L, Waghorn G.C, Thomson N.A (2006) Supplementing dairy cows with oils to improve performance and reduce methane—Does it work? In: Proceedings of

Proceedings of the New Zealand Society for Animal Production, 65:4-8.

circuit indirect calorimetry system. Can. J. Anim. Sci. 74:65-72.

dairy heifers. J. Dairy Sci. 68:1297-1301.

from cattle. J. Environ. Qual. 35:1686-1691.

dairy cows. J. Agric. Sci. 129:459-469.

ruminants. Brit. J. Nutr. 19:511-522.

production in ruminants. Anim. Sci. Technol. 64:790-796.

of Holstein cows by several equations. J. Dairy Sci. 78:2402-2414.

Reproduction, Stuttgart, Germany, pp. 333-348.

and their slurry. Environ.l Monit. Assess. 107:329-350.

the New Zealand Society of Animal Production, 66:176-181.

Res. 40:283-295.

Sci. 78:2760-2766.

Production, 57:130-133.

16:404-419.


[51] Baldwin R.L, Thornley J.H.M, Beever D.E (1987) Metabolism of the lactating cow. Digestive elements of a mechanistic model. J. Dairy Res. 54:107-131.

Reducing Enteric Methane Losses

from Ruminant Livestock – Its Measurement, Prediction and the Influence of Diet 149

[69] Yan T, Mayne C.S (2007) Mitigation strategies to reduce methane emission from dairy cows. In: High Value Grassland: Providing Biodiversity, a Clean Environment and

[70] Reynolds C.K, Crompton L.A., Mills J.A.N (2011) Improving the efficiency of energy

[71] IPCC (Intergovernmental Panel on Climate Change) (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual. Cambridge

[72] IPCC (2006) 2006 IPCC guidelines for national greenhouse gas inventories. Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T. and Tanabe, K. (Eds.), Agriculture, Forestry and other Land Use, Volume 4. Institute for Global Environmental Strategies (IGES),

[73] Bell M.J, Wall E, Simm G, Russell G (2011) Effects of genetic line and feeding system on methane emissions from dairy systems. Anim. Feed Sci. Technol. 166-167:699-707 [74] DeRamus H.A, Clement T.C, Giampola D.D, Dickison P.C (2003) Methane emissions of beef cattle on forages: efficiency of grazing management systems. J. Environ. Qual.

[75] Giger-Reverdin S, Sauvant D (2000) Methane production in sheep in relation to concentrate feed composition from bibliographic data. Cahiers Options

[76] Okine E.K, Mathison G.W, Hardin R.T (1989) Effects of changes in frequency of reticular contractions on fluid and particulate passage rates in cattle. Can. J. Anim. Sci. 67:3388-

[77] Yates C.M, Mills J.A.N, Kebreab E, Crompton L.A, France, J (2001) An integrated modelling approach to providing cost-effective means of reducing methane emissions

[78] Huque K.S, Chowdhury S.A (1997) Study on supplementing effects or feeding systems of molasses and urea on methane and microbial nitrogen production in the rumen and growth performances of bulls fed a straw diet. Asian-Austral. J. Anim. Sci. 10:35-46. [79] Moss A (1992) Methane from ruminants in relation to global warming. Chemistry

[80] Lovett D.K, Lovell S, Stack L, Callan J, Finlay M, Conolly J, O'Mara F.P (2003) Effect of forage/concentrate ratio and dietary coconut oil level on methane output and

[81] McGinn S.M, Beauchemin K.A, Coates T, Colombatto D (2004) Methane emissions from beef cattle: Effects of monensin, sunflower oil, enzymes, yeast and fumaric acid. J.

[82] Beauchemin K.A, McGinn S.M (2006) Methane emissions from beef cattle: Effects of

[83] Jordan E, Lovett D.K, Monahan F.J, Callan J, Flynn B, O'Mara F.P (2006) Effect of refined coconut oil or copra meal on methane output and performance of beef heifers. J.

[84] Dohme F, Machmüller A, Wasserfallen A, Kreuzer M (2000) Comparative efficiency of various fats rich in medium-chain fatty acids to suppress ruminal methanogenesis as

performance of finishing beef heifers. Livest. Prod. Sci. 84:135-146.

fumaric acid, essential oils, and canola oil. J. Anim. Sci. 84:1489-1496.

measured with RUSITEC. Can. J. Anim. Sci. 80:473-782.

Premium Products. University of Keele, Staffordshire, UK, pp. 345-348.

utilisation in cattle. Anim. Prod. Sci. 51:6-12.

University Press, Cambridge, UK.

Hayama, Japan.

32:269-277.

1989.

Méditerranéennes 52:43-46.

Industry 9:334-336.

Anim. Sci. 82:3346-3356.

Anim. Sci. 84:162-170.

from dairy cows. J. Agric. Sci. 137:120-121.


[69] Yan T, Mayne C.S (2007) Mitigation strategies to reduce methane emission from dairy cows. In: High Value Grassland: Providing Biodiversity, a Clean Environment and Premium Products. University of Keele, Staffordshire, UK, pp. 345-348.

148 Livestock Production

Anim. Sci. 74:226-244.

pp. 383-406.

19:611-613.

134.

Dairy Sci. 75:2165-2175.

Monit. Assess. 42:133-141.

Society for Animal Science, 94.

technique. Can. J. Anim. Sci. 82:201-206.

Anim. Res. 55:1-23.

[51] Baldwin R.L, Thornley J.H.M, Beever D.E (1987) Metabolism of the lactating cow.

[52] Lescoat P, Sauvant D (1995) Development of a mechanistic model for rumen digestion

[53] Pitt R.E, van Kessel J.S, Fox D.G, Pell A.N, Barry M.C, van Soest P.J (1996) Prediction of ruminal volatile fatty acids and pH within the net carbohydrate and protein system. J.

[54] Kohn R.A, Boston R.C (2000) The role of thermodynamics in controlling rumen metabolism. In: McNamara J.P, France J, Beever D.E (Eds.), Modelling Nutrient

[58] Dijkstra J, Bannink A, van der Hoek K.W, Smink W (2006) Simulation of variation in

[59] Offner A, Sauvant D (2006) Thermodynamic modelling of ruminal fermentations.

[63] Bratzler J.W, Forbes E.B (1940) The estimation of methane production by cattle. J. Nutr.

[64] Holter J.B, Young A.J (1992) Methane prediction in dry and lactating Holstein cows. J.

[65] Johnson D.E, Ward G.M (1996) Estimates of animal methane emissions. Environ.

[66] Yates C.M, Cammell S.B, France J, Beever D.E (2000) Prediction of methane emissions from dairy cows using multiple regression analysis. In: Proceedings of the British

[67] Boadi D, Wittenberg K.M (2002) Methane production from dairy and beef heifers fed forages differing in nutrient density using the sulphur hexafluoride (SF6) tracer gas

[68] Yan T, Mayne C.S, Porter M.G (2005) Effects of dietary and animal factors on methane production in dairy cows offered grass silage-based diets. In: Proceedings of the 2nd Greenhouse Gases and Animal Agriculture Conference, Zurich, Switzerland, pp.131-

[60] Moe P.W, Tyrrell H.F (1979) Methane production in dairy cows. J. Dairy Sci. 62:1583. [61] Yan T, Agnew R.E, Gordon F.J, Porter M.G (2000) Prediction of methane energy output in dairy and beef cattle offered grass silage-based diets. Livest. Prod. Sci. 64:253-263. [62] van Straalen W.M (2005) Voorspelling van de methaanproductie op een aantal praktijkbedrijven op basis van de rantsoensamenstelling en productieniveau.

methane emission in dairy cattle in The Netherlands. J. Dairy Sci. 89:259.

Schothorst Feed Reseatrch, Lelystad, The Netherlands.

validated using duodenal flux of amino acids. Reprod. Nutr. Dev. 35:45-70.

Utilization in Farm Animals. CAB International, Wallingford, UK, pp. 11-24. [55] Giger-Reverdin S, Morand-Fehr P, Tran G (2003) Literature survey of the influence of dietary fat composition on methane production in dairy cattle. Livest. Prod. Sci. 82:73-79. [56] van Laar H, van Straalen W.M (2004) Ontwikkeling van een rantsoen voor melkvee dat de methaanproductie reduceert. Schothorst Feed Reseatrch, Lelystad, The Netherlands. [57] Danfær A, Huhtanen P, Udén P, Sveinbjornsson J, Volden H (2006) The nordic dairy cow model, Karoline. In: Kebreab E, Dijkstra J, France J, Bannink A Gerrits W.J.J (Eds.), Modelling Nutrient Utilization in Farm Animals. CAB International, Wallingford, UK,

Digestive elements of a mechanistic model. J. Dairy Res. 54:107-131.


Feed Sci. Technol. 116:301-311.

Granada, Spain, pp. 339-342.

emissions from a feedlot. Can. J. Anim. Sci. 84:445-453.

technique. Environ. Sci. Technol. 28:359-362.

fat added to the diet. J. Anim. Sci. 76:906-914.

with Grain. J. Dairy Sci. 91:1159–1165.

J. Dairy Sci. 93:5300–5308.

current status and future outlook. J Dairy Sci. 84:194-203.

from lespedeza. Anim. Feed Sci. Technol. 144:212-227.

methanogenesis. Aust. J. Exp. Agric. 48:7-13.

production in lactating dairy cows. J. Dairy Sci. 90:1781-1788.

[85] Chaudhry A.S, Khan M.M.H (2010) Effect of various spices on in vitro degradability, methane and fermentation profiles of different ruminant feeds. In: Proceedings of the 4th Greenhouse Gases and Animal Agriculture Conference, 3-8 October, Banff, Canada. [86] Mohammed N, Onodera R, Itabashi H, Ara Lila Z (2004) Effects of ionophores, vitamin B6 and distiller's grains on in vitro tryptophan biosynthesis from indolepyruvic acid, and production of other related compounds by ruminal bacteria and protozoa. Anim.

[87] Boadi D.A, Wittenberg K.M, Scott S.L, Burton D, Buckley K, Small J.A, Ominski K.H (2004) Effect of low and high forage diet on enteric and manure pack greenhouse gas

[88] Odongo N.E, Bagg R, Vessie G, Dick P, Or-Rashid M.M, Hook S.E, Gray J.T, Kebreab E, France J, McBride B.W (2007) Long-term effects of feeding monensin on methane

[89] Johnson K.A, Huyler M.T, Westberg H.H, Lamb B.K, Zimmerman P (1994a.) Measurement of methane emissions from ruminant livestock using a SF6 tracer

[90] Johnson D.E, Abo-Omar J.S, Saa C.F, Carmean B.R (1994b) Persistence of methane suppression by propionate enhancers in cattle diets. In: Aquilera, J.F. (Ed.), Energy Metabolism of Farm Animals. EAAP Publication No. 76. CSIC Publishing Service,

[91] Sauer F.D, Fellner V, Kinsman R, Kramer J.K.G, Jackson H.A, Lee A.J, Chen S (1998) Methane output and lactation response in Holstein cattle with monensin or unsaturated

[92] McGuffey R.K, Richardson L.F, Wilkinson J.I.D (2001) Ionophores for dairy cattle:

[93] Grainger C, Auldist M.J, Clarke T, Beauchemin K.A, McGinn S.M, Hannah M.C, Eckard R.J, Lowe L.B (2008) Use of Monensin Controlled-Release Capsules to Reduce Methane Emissions and Improve Milk Production of Dairy Cows Offered Pasture Supplemented

[94] Grainger C, Williams R, Eckard R.J, Hannah M.C (2010) A high dose of monensin does not reduce methane emissions of dairy cows offered pasture supplemented with grain.

[95] Grainger C, Clarke T, Auldist M.J, Beauchemin K.A, McGinn S.M, Waghorn G.C, Eckard R.J (2009). Potential use of Acacia mearnsii condensed tannins to reduce methane emissions and

[96] Animut G, Puchala R, Goetsch A.L, Patra A.K, Sahlu T, Varel V.H, Wells J (2007) Methane emission by goats consuming diets with different levels of condensed tannins

[97] McAllister T.A, Newbold C.J (2008) Redirecting rumen fermentation to reduce

[98] Beauchemin K.A, Kreuzer M, O'Mara F, McAllister T.A (2008) Nutritional management

nitrogen excretion from grazing dairy cows. Can. J. Anim. Sci. 89:241-251.

for enteric methane abatement: a review. Aust. J. Exp. Agric. 48:21-27.

## *Edited by Khalid Javed*

Innumerable publications on livestock production are available in the world market. The book under discussion has not been produced to burden the market with another such publication rather it has been brought out employing a novice format to meet the requirements of students, researchers who are working in different parts of the world in different environments.

Livestock Production

Livestock Production

*Edited by Khalid Javed*

Photo by Straitel / iStock