Lowering ruminant methane emissions through improved feed conversion efficiency

Lowering ruminant methane emissions through improved feed conversion efficiency

Animal Feed Science and Technology 166–167 (2011) 291–301 Contents lists available at ScienceDirect Animal Feed Science and Technology journal homep...

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Animal Feed Science and Technology 166–167 (2011) 291–301

Contents lists available at ScienceDirect

Animal Feed Science and Technology journal homepage: www.elsevier.com/locate/anifeedsci

Lowering ruminant methane emissions through improved feed conversion efficiency G.C. Waghorn a,∗ , R.S. Hegarty b a b

DairyNZ, cnr Ruakura & Morrinsville Roads, Hamilton 3240, New Zealand Beef Industry Centre of Excellence, Industry and Investment NSW, Travenna Road, Armidale 2351, NSW, Australia

a r t i c l e

Keywords: Feed efficiency Methane Residual feed intake Emissions intensity RFI

i n f o

a b s t r a c t Improvements in feed conversion efficiency (FCE) can be applied to individual animals as well as to production from land, as in a farm system. Our focus relates mainly to food production from individual animals within any animal population where there is divergence in the efficiency that individuals use ingested feed for maintenance and production; primarily due to differences in digestion and metabolism. Intake variation from the predicted mean for individuals of a similar size and level of production in a population has been termed residual feed intake (RFI), with low values indicating an efficient animal. Efficient animals require less feed than average and can be expected to produce less CH4 and N2 O per unit product than the population average at a similar level of production. Selection for this trait will lower CH4 emissions per animal, unless more animals are kept to eat the feed not required by efficient animals. There are few published evaluations of CH4 yields from animals with divergent RFI and there is little evidence that efficient animals have a different CH4 yield expressed as CH4 /kg dry matter (DM) intake. Of equal or greater importance than RFI is the need to select high producing animals, as this will reduce emissions/unit of product, referred to as emissions intensity (Ei). Research should identify productive individuals that have a low RFI to minimise Ei and maintain food production. The extent to which CH4 can be reduced by selection for RFI will depend on the heritability of efficiency, dispersal of efficient animals through all populations and their resilience in a production system (i.e., robustness). The benefit of RFI to lowering greenhouse gas (GHG) emissions is its application, irrespective of farming system (i.e., confined, intensive, extensive grazing), especially because efficient animals are likely to increase farm profitability. Efficient animals are already in all herds and flocks and research must identify and remove inefficient individuals, while retaining and ensuring efficient ones are fit to purpose. However, the biggest benefits to reducing emissions and increasing production will be associated with good animal management practice (e.g., appropriate genetics, reproductive performance, longevity) with efficient animals superimposed. Good animal systems management will improve profitability, and apply to both intensive and extensive systems to increase food production and lower Ei. One dilemma for agriculturists will be the practice of feeding grains to ruminants, as gains in animal efficiency, especially in reduction of Ei, are likely to

Abbreviations: Ei, emissions intensity (g/product); FCE, feed conversion efficiency; GHG, greenhouse gas; LW, liveweight; ME, metabolizable energy; MY, methane yield; OM, organic matter; RFI, residual feed intake. ∗ Corresponding author. Tel.: +64 7 858 3881; fax: +64 7 858 3571. E-mail address: [email protected] (G.C. Waghorn). 0377-8401/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.anifeedsci.2011.04.019

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be biggest with high energy density rations, but feeding grain to ruminants may become an unsustainable practice if food supplies for humans are limited. This paper is part of the special issue entitled: Greenhouse Gases in Animal Agriculture – Finding a Balance between Food and Emissions, Guest Edited by T.A. McAllister, Section Guest Editors: K.A. Beauchemin, X. Hao, S. McGinn and Editor for Animal Feed Science and Technology, P.H. Robinson. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Efficiency of feed utilisation can be described by a number of metrics which combine measures of feed consumption and desired animal production. Of these, residual feed intake (RFI), is being widely adopted for genetic improvement of ruminants (Archer et al., 1999). Recognition that feed consumption also generates an undesirable animal product (i.e., enteric CH4 ) has seen efficiency of conversion of feed into enteric CH4 (or CH4 yield; MY) emerge as an important consideration and potential animal and plant breeding objective (Molano and Clark, 2008). As the long term viability of animal production may be dependent upon the balance of desirable and undesirable outputs, the term emission intensity (Ei; CH4 /unit animal product) has been created (Leslie et al., 2008). The interplay of the indices RFI, MY and Ei is shown in Fig. 1. While the metrics of RFI, MY and Ei are typically calculated at the individual animal level, they can equally be applied at the whole farm level. This review will interpret opportunities for influencing these outputs and indices at both the individual animal and farm system level through changes in feed efficiency using RFI as the metric of choice. 2. Feed conversion efficiency in ruminant systems Extensive grazing systems have fewer inputs, lower costs and fewer options for greenhouse gas (GHG) mitigation than intensive animal production systems (Eckard et al., 2010). However, management of feed and animal genetics bases are potential points of intervention for affecting production and profitability, as well as to reduce Ei from extensive (Bentley et al., 2008) and intensive (Donoghue et al., 2009) ruminant livestock systems. Changes in efficiency of feed utilisation can improve production and alter CH4 emissions. Feed accounts for 66–77% of costs in beef cow/calf and feedlot finishing systems in North America (Williams and Jenkins, 2006) and about 50% of production costs in New Zealand pastoral dairying systems (DairyBase, 2009; Beever and Doyle, 2007). In grazing systems, efficient feed use is a function of harvest of available DM by animals (Orr et al., 2010) and efficiency of conversion of ingested feed to product. Animal and farm management practices that affect feed use efficiency include multiple aspects of animal genotype, available feed and animal health, and reproductive management (Table 1). While Table 1 is not exhaustive, these variables are important because they impact feed use efficiency, as well as profitability, which is the principal driver of ruminant agriculture in the developed world. Food creation as animal products is commonly reported as weight of feed consumed per weight of animal product (e.g., liveweight (LW), carcass weight, milk production) generated (i.e., feed conversion ratio) or its inverse ‘product/unit feed’ which is referred to as gross efficiency. While these are simple to calculate, they are not suitable as traits for genetic improvement of feed use efficiency in ruminants as they include correlated changes in intake and growth (Arthur et al., 2001a,b). For this reason, RFI is the preferred metric for genetic improvement in feed use efficiency. It is essential that selection for low RFI animals has no adverse effect on animal productivity. Research has not demonstrated important negative associations with RFI, but it will be important to monitor economically relevant traits. For example, cattle with superior breeding values for RFI (i.e., efficient individuals) have less body fat, especially rib fat (Herd and Arthur, 2009; Egarr et al., 2009), and implications of this for post-partum anoestrus in the grazing herd could be negative. The risks of selection for one trait, such as milk yield, while ignoring others such as fertility, are well known, especially Dry Matter Intake

RFI

MY

Animal Production

Methane Production

Ei Fig. 1. Linkages between feed consumption and the output of desirable and undesirable (methane) animal products through residual feed intake (RFI), methane yield (MY) and emission intensity (Ei).

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Table 1 Animal and key farming system variables that affect feed conversion efficiency of ruminants and consequently the emissions intensity of the farming system. Animal options Species – sheep beef, dairy Stage of maturity – affects energy partitioning Genetic potential – (production, RFI) selection for profitable traits Environmental adaptation – productive and reproductive resilience Good grazers; seek out feed and water, even if substantial walking is needed. Low susceptibility to disease (ticks, internal parasites, etc.), avoid toxic forages Farm system options Extensive grazing – options often dictated by needs for sustainability and need to accommodate adverse conditions (feed supply) with minimal inputs Provision of pastures or browse able to match animal energy and nitrogen requirements Use of tested sires at regionally appropriate male:female ratio Pregnancy scanning and removal of non-pregnant females Seasonal supplementation of females to ensure return to service and of progeny to minimise time to finish or mating Manage for minimal parasite impact. Good mothering and high weaning rate; good growth rates Intensive grazing – use of improved forages, ready access to livestock, ability to supplement and manage to achieve a high production/ha Production affected by forage type and grazing management to maximise pasture harvest Optimal forage species to maximise feeding value Use of additives, rumen modifiers and supplements to ensure adequate feeding at all times High reproduction, early mating, high conception and weaning rates Attendance to health, good longevity to minimise replacement rate Select animals that are adapted and have good genetic merit under forage grazing Intensive feedlot – for (dairy) production or finishing for carcass production Optimal ration formulation and adaptation to finishing ration Appropriate parasite and disease control on induction Aware of heat stress risk & shade/sprinklers provided as appropriate

in the dairy industry (Wall et al., 2007; McDougall, 2006), and care must be taken to monitor all aspects of animal health, reproduction and production in selection. 2.1. Residual feed intake (RFI) Efficient animals (i.e., low or negative RFI) eat less than the average predicted based on defined LW, physiological state and production, whereas inefficient (i.e., high RFI) individuals eat more than the predicted average. Selection for divergence in RFI identifies individuals which differ in feed requirements, but is independent of LW or productivity. Selection based on RFI has been exploited in genetic improvement programmes with non-ruminant species for decades and has resulted in fast growing animals requiring less feed (Hermesch, 2004). RFI is moderately heritable in beef animals (h2 = 0.14–0.46, Arthur et al., 2001a,b; Pitchford, 2004; Arthur and Herd, 2005; Basarab et al., 2005; Lancaster et al., 2009) with differences in DM intake of ∼15% after two generations of selection (Herd et al., 2002a). Testing for RFI is becoming more common in the North American beef industry where grain and silage based diets are fed. In contrast to fresh pasture, these diets are higher in DM and energy content, less bulky, do not deteriorate appreciably when fed once or twice daily, and have a consistent chemical composition over the entire feeding period. While the RFI trait is conserved in grazing animals (Herd et al., 2002a), more data are needed to correlate RFI of cattle measured with grain and silage based diets with RFI of cattle grazing pasture. Current protocols for RFI determination in cattle require individual animal identification and accurate measurement of intake as well as production (i.e., LW gain). Under intensive systems, feed is placed in bins on load cells with continuous data capture, and animals feeding from each bin are identified by electronic identification. The cost of determining RFI is primarily that of the equipment required and data interrogation. However measurement of RFI from cattle that are on pasture incurs substantial additional costs because of investment in equipment and provision of suitable forage. This may be green chop or dried forage that enables more accurate measures of DM intake, and costs are compounded by the duration of intake measurements (e.g., ∼70 d for beef cattle; Archer et al., 1997). However, increasing weighing frequency from 7–14 d to 2–3 d can reduce the measurement period to about 50 d (G.C. Waghorn, DairyNZ, Hamilton, New Zealand; unpublished data) and use of week-on/week-off data collection would double the number of animals measured per period (Donoghue et al., 2009). High measurement costs have also prompted searches for genetic markers to identify efficient individuals more easily (Chen et al., 2009). Current research is extending measurements of RFI to dairy cows fed forages (Carnie et al., 2010). In that study, RFI for milk production was determined by measuring RFI for daily gain in pre-pubertal dairy heifers fed alfalfa cubes. Use of daily gain as a proxy for lactation efficiency is based on two assumptions, being that divergence will apply to metabolizable energy (ME) requirements for maintenance, which accounts for more than half a dairy cow’s lifetime feed needs (Freer et al., 2007), and that RFI has a physiological/biochemical basis so that efficiency for gain will also apply to milk synthesis. Once selected, efficient individuals require less feed for the same level of production (Table 2) and this will lower Ei (Table 3). At the farm level, a consequence of lower feed requirement per individual may be to increase stocking rate (i.e., animals/hectare). Thus, under this scenario, total GHG would not decline, although there would be more production from the same land area.

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Table 2 Examples of differences in feed intake (kg DM/d) of cattle identified with divergent residual feed intake (RFI) at similar levels of productivity. Animal age, type and average daily gain (ADG)a

Low RFI (Efficient)

High RFI (Inefficient)

Sourceb

Steers, 7–8 months; ADG 1.5 kg/d Cows at maintenance Progeny of divergent cows; ADG 1.3 kg/d Bulls, ADG 1.75 kg/d Steers, ADG 0.97 kg/d Calves, ADG 1.06 kg/d Calves, finishing 1.42 kg/d Steers, finishing 1.48 kg/d Steers, finishing 1.12 and 1.22 kg/dc Steers, finishing 1.48 and 1.46 kg/d Steers, finishing 1.11 and 1.07 kg/d Bulls, ADG 1.42 and 1.40 kg/d

8.00 13.1 8.3 9.60 7.27 8.73 8.45 11.7 8.38 9.62 10.4 8.62

8.93 16.5 9.1 12.00 10.13 10.86 10.63 12.7 14.13 11.62 11.1 10.28

1 2 2 3 4 4 4 5 6 7 8 9

a A single value for average daily gain (ADG) indicates a similar value for low and high RFI animals, two values correspond to ADG for low and high RFI respectively. b 1: Basarab et al. (2003); 2: Basarab (2005); 3: Fox et al. (2004); 4: Carstens and Tedeschi (2006); 5: Hegarty et al. (2005); 6: Hegarty et al. (2007); 7: Nkrumah et al. (2006); 8: Herd et al. (2009); 9: Lancaster et al. (2009). c Animals used for CH4 measurement, summarised in Table 4.

It is important that animals selected for low RFI (i.e., requiring less feed than predicted) have been selected for high productivity, whether this be as daily gain or milk production. While RFI is independent of production, high producing animals are the most profitable because they create more product and maintenance energy costs are diluted relative to lower producers (Table 3). For this reason, Arthur and Herd (2005) proposed a 2 stage selection, to identify animals with a high genetic merit, with only the top 10–20% most productive bulls being tested for RFI. 2.1.1. Physiology of RFI The bases for differences in RFI have been ascribed to the physiology and biochemistry associated with digestion, energy capture (i.e., as ATP) and energy utilisation, but these have not been studied in detail. In their analyses of Angus steers selected for divergent RFI, Herd and Arthur (2009) examined feed intake, feed digestion, metabolism, physical activity and thermal regulation. They were able to explain 73% of the variation in RFI, and attributed it to variation in protein turnover, tissue metabolism and stress (37%), with lesser contributions from digestibility (10%), heat increment and fermentation (9%), physical activity (9%) and body composition (5%). One reason advanced by Basarab et al. (2003) for lower energy requirements of efficient steers was the smaller visceral mass (i.e., stomach, intestines, liver) in animals with low RFI (51.1 kg versus 55.3 kg for inefficient steers). Although a smaller mass would lower energy expenditure, it is hard to reconcile with higher digestibility. Higher digestibility can result from a longer rumen retention time and a larger digesta pool, traits that are typically associated with a larger rumen. Recent measurements from divergent Angus-Hereford steers by Cruz et al. (2010) did not identify differences in visceral mass. Differences in digestibility among animals fed the same diet offers potential for creating divergence in RFI because of a higher nutrient energy yield, but more digestion is likely to increase CH4 yields as more H2 derived from feed will be used to create CH4 . Although Richardson and Herd (2004) suggested a divergence of only 2% in DM digestion (i.e., higher in efficient steers), Nkrumah et al. (2006) reported means of 75.3%, 73.4% and 70.9% for high, medium and low feed efficient steers. In his review of 80 studies, Titgemeyer (1997) reported variation among individuals within experiments of 1.2–17.4% in rumen digestion and 0.4–9.1% for total tract digestibility. Thus there appears opportunity to affect nutrient availability and methanogenesis by selecting for efficiency of digestion. Variation in rumen function among individuals fed the same diet was reviewed in relation to RFI in cattle by Waghorn and Dewhurst (2007). There is evidence that individual animals harbour their own specific microflora, as demonstrated by the heritability of susceptibility to legume bloat (Morris et al., 1991), and differences in rates of ruminal in sacco feed degradation among animals fed the same diet (Weimer et al., 1999). Variation may include the extent and duration of digestion in the rumen (i.e., residence time), which appears to be a heritable trait in sheep (Smuts et al., 1995), which influences quantities Table 3 An illustration of changes in emissions intensity (Ei; CH4 /kg live weight gain) in sheep fed diets of varying quality. Dietary ME (MJ/kg DM)

Forage

Gain (g/d)

Methane (g/kg DM intake)

Feed:gain ratio (kg DM intake/kg gain)

Ei (g/kg gain)

10.0 11.0 12.0 11.5 12.0

Ryegrass pasture Ryegrass pasture Ryegrass pasture Lucerne Sullaa

100 150 200 250 300

24.0 22.0 21.0 20.0 17.5

13.6 9.4 7.5 6.7 6.2

300 210 160 130 110

ME: metabolisable energy; DM: dry matter. Calculated CH4 emissions per unit of liveweight gain from growing lambs fed forages with a range of feeding values (from Waghorn and Clark, 2006). a Sulla (Hedysarum coronarium) contains condensed tannins.

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Table 4 Feed dry matter (DM) intakes and CH4 production from cattle selected for variance in residual feed intake (RFI). Low RFI are efficient, high RFI are inefficient. Number of cattle RFI Steers Steers Cowsa Cowsb

10 + 10 11 + 8 8+8 8+8

Dry matter intakes

Production (kg/d)

CH4 production (g/d)

g CH4 /kg DM intake

Low

High

P

Low

High

P

Low

High

P

Low

High

P

8.38 8.65 10.8 12.2

14.13 8.68 11.2 12.5

0.001 0.39 0.57 0.61

1.13 1.48 na na

1.23 1.46 na na

ns – – –

131 97.5 191.8 272.1

173 129.3 193.9 258.7

0.09 0.04 0.78 0.39

16.3 11.3 18.3 22.4

14.7 14.9 17.5 20.9

0.37 0.04 0.40 0.21

Source

1 2 3 3

References and methodology: 1: Hegarty et al. (2007); 590 kg steers fed diets containing 750 g/kg grain (DM basis). 2: Nkrumah et al. (2006), 500 kg steers fed diets containing 80% grain (DM basis) with CH4 measured 16 h/d without access to feed, Daily gain was from previous feedlot trials where intakes were 9.6 and 11.6 kg DM/d for efficient and inefficient animals. 3: Waghorn et al., DairyNZ, Hamilton, New Zealand; unpublished data. a Nonlactating Holstein/Friesian aged 21 months fed alfalfa cubes. b Holstein/Friesian aged 26 months at day 60 of lactation fed pasture.

and proportions of volatile fatty acids, proteolysis and deamination of amino acids, microbial growth and methanogenesis (Janssen, 2010). Differences in products of digestion and rumen outflow affect H2 availability for methanogenesis (Janssen, 2010), and energy capture efficiency as high energy phosphate bonds by ruminal microorganisms (Waghorn and Dewhurst, 2007). 2.1.2. Gains in productivity through RFI Differences in DM intake and productivity of beef cattle with divergent RFI are indicated by examples in Table 2. Differences between selections are substantial, with the magnitude of the differences dependant on the proportion of the screened animals selected, with differences between the most and least efficient individuals biggest when they comprise 5% as opposed to 20% of the population. Furthermore, the extent to which feed intakes can be reduced by selection is finite, because energy must be allocated to maintenance and synthesis of meat and milk. Selection for RFI is independent of product composition, and so the amount of energy per unit product cannot be altered. The extent to which individual intake is reduced over generations depends on the heritability of the trait and the selection pressure applied. Mitigation of livestock GHG emissions will be affected by the rate and extent to which the trait can be disseminated throughout a population. Benefits of selecting sires as opposed to dams for improving RFI are clear, especially when artificial insemination is in widespread use. However, it is important to appreciate that efficiency gains in a herd or flock from selecting efficient animals will be about half of that indicated by the breeding value for RFI, unless retention of efficient animals is accompanied by removal of inefficient individuals. The average feed intake by individuals in a population comprises that from animals with all levels of efficiency, and so improvement is best achieved by animal culling as well as retention. Furthermore, gains in progeny feed efficiency will be one half of that indicated by the RFI breeding values of the parents. In general, the impact of selection for RFI on dairy cattle will be less than for beef cattle because a higher proportion of feed energy will be expressed in milk than is retained in LW gain. The energy in a product cannot be affected by variation in RFI (i.e., conservation of energy), and product composition is largely independent of RFI (Arthur et al., 2001a,b; Richardson and Herd, 2004; Lancaster et al., 2009). The energy content of milk and LW gain vary in response to proportions of fat, protein and water (e.g., affected by diet, stage of lactation, age of animal) but these differences will be similar for low and high RFI selections. Typical values for 1 kg empty LW gain in growing cattle are 19–24 MJ/kg, representing about 22% of ME intake (Freer et al., 2007). In contrast, the energy in milk of cows producing 20–30 kg/d represents 35–41% of their respective ME intakes (Freer et al., 2007). Only the energy used for product synthesis and animal maintenance is able to be manipulated through selection, but this example suggests that about 78% of ME is amenable to selection in growing cattle, compared to 59–65% of ME in lactating cows. 3. Methanogenesis from animals selected for divergent RFI Three studies where CH4 production was measured from animals selected for divergent RFI are summarised in Table 4. Measurements made with steers have used either high release rate of SF6 over 5 d (Hegarty et al., 2007), or 16 h measurements in respiration hoods repeated at 3 d intervals (Nkrumah et al., 2006). More recent measurements from cows have been made over 48 h in respiration calorimeters (G.C. Waghorn, DairyNZ, Hamilton, New Zealand; unpublished data). Values from these studies (Table 4), varied from about 11 to 22 g CH4 /kg DM intake with data being derived from concentrate based diets containing 75–80% grain fed to steers as well as alfalfa cubes and fresh ryegrass pasture fed to dairy heifers. This data should be interpreted with care, especially where feed intake was restricted during CH4 measurements, because emissions may be twice as high during and immediately after eating, than at other times (G.C. Waghorn, DairyNZ, Hamilton, New Zealand; unpublished data). The CH4 yield was substantially lower from cattle fed concentrate diets over 16 h (Nkrumah et al., 2006) and, although Hegarty et al. (2007) showed a relationship between CH4 production (MP, g/d) and RFI (kg/d): MP (g/d) = 13.3 × RFI + 179.5; P=0.002, R2 = 0.12, there were differences in CH4 yield among animals selected for varying RFI

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Table 5 Emissions, expressed as CO2 -e, associated with production of foods for human consumption in the United Kingdom. Food

Milk Eggs Chicken meat Pork Beef Sheep meat Wheata Peasa

Type of emissions (g/kg product)

Emissions expressed as CO2 -e (kg)

CH4 /NH3 /N2 O

/kg product

/MJ edible product

/kg edible protein

19/3/1 8/28/4 5/23/3 49/28/2 265/71/12 301/41/11

1.0 3.8 3.5 4.7 14.7 15.8 0.2–0.8b 0.1

0.37 0.57 0.40 0.30 1.40 1.51 0.01–0.04 0.005

28.6 33.3 18.4 34.2 93.5 92.9 0.1–0.4 0.04

From DEFRA (2008) and Gill et al. (2010). a Derived from Canadian research by Khakbazan et al. (2009) and excludes soil carbon losses from cultivation. b Range is due to variations in inputs and grain yield.

(Table 4). Daily CH4 production from cattle with low RFI (i.e., efficient animals) likely declines as DM intake is reduced, but more information is needed to confirm effects of RFI on CH4 yield from cattle fed high concentrate diets. Cattle of divergent RFI fed equal DM intake of a high concentrate diet did not differ in CH4 production (Hegarty and Herd, 2008). Selection for low RFI should result in ruminants which produce less CH4 at similar levels of production, so that their Ei should be lower (Tables 3 and 5) as less feed will be required for production. Reductions in emissions and Ei with improved RFI should also apply to N2 O (Herd et al., 2002b), as lower N intakes will result in less urinary N excretion and reduce N excretion/unit production. Consequently, selection for RFI is beneficial for producers, consumers and the environment (Beukes et al., 2010) because more food is produced with less GHG emissions and more profit. RFI can be applied to both intensive and extensive systems and to all animals used for food production. 4. Emissions intensity and food production Ruminant production systems have a high Ei relative to pork and poultry production systems (Table 5), largely because of enteric CH4 generation (DEFRA, 2008). A low Ei or ‘greenhouse cost’ per unit of animal product is increasingly regarded as a sustainability criterion by livestock producers and consumers, and the value of ruminants for their contribution to global food supply and GHG is under scrutiny. There are two main issues concerning use of Ei to manage ruminant production and prioritise feeds used for food production, being the GHG emissions associated with production of edible food (milk, meat) and the amount of human edible feed that is fed to ruminants (mainly grains) to create ruminant products. A simple summary (Table 3) relating CH4 production and Ei to sheep growth demonstrates the importance of rate of gain, forage quality and dilution of maintenance costs to lowering Ei. However, evaluation of farming systems based on life cycle analyses, summarised by Beauchemin et al. (2010), indicated the level of complexity and range of assumptions required to calculate, monitor and estimate emissions in complex farming systems. In their analysis of Canadian beef production, Beauchemin et al. (2010) attributed 63% of total GHG emissions to enteric CH4 production, with N2 O accounting for 27%. These and other life cycle analyses highlight the importance of factors such as longevity and reproductive efficiency to dilute GHG emissions associated with maintenance of the dam, as well as the need for rapid growth (Table 3) and low RFI to minimise emissions from offspring grown for slaughter (Alford et al., 2006; Alcock and Hegarty, 2011). Emissions of GHG from ruminants are in Table 5, with values for pork, chicken and other meats. The lower emissions from non-ruminants are evident and, although life cycle analyses are central to calculation of Ei, as the assumptions and inputs play a major role in determining outcomes, especially when comparing systems. Gill et al. (2010) report a doubling of the calculated livestock emissions from 9% of global anthropogenic GHG to about 18% when all input costs, including land use change and food processing, are considered. As a consequence, there are wide variations in predictions of Ei associated with the same level of milk production (i.e., 0.5–1.5 kg CO2 -e/kg milk (Martin et al., 2010)). Similar variation is evident for beef production, with a range from 17 to 37 kg CO2 -e/kg carcass, or a mean of about 33 kg CO2 -e/kg edible cuts (Beauchemin et al., 2010). Use of Ei enables a balance to be achieved between demand for food and the GHG costs associated with its production, but analyses nearly always ignores losses of soil C associated with cultivation unless there is a change of land use. These losses, often 1000–2000 kg C ha/yr, represent a depletion of soil organic matter (OM), but are lessened with minimal tillage systems (Reicosky et al., 1995). However, depletion of soil OM in many cultivated soils demonstrates an overall loss of soil C to the atmosphere when grains are grown. These losses are only taken into account in inventory if there is a change in land use, and they are often ignored in life cycle analyses. Indeed the high productivity and lower CH4 yield associated with high grain diets (>80% of DM intake) fed to ruminants (e.g., Beauchemin et al., 2010) suggests grain feeding could be a way to reduce Ei relative to forages but, in view of C losses from cultivated soils, this premise should be challenged with new research. A detailed analysis of a wheat/pea cropping system in Canada by Khakbazan et al. (2009), showed GHG emissions associated with wheat production (average annual yield 2500 kg/ha) ranged from 0.2 to 0.8 kg CO2 -e/kg. Emissions from peas

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(0.1 kg CO2 -e/ha) were much lower because less fertilizer N was used, but neither of these analyses took losses of soil OM into account, or other GHG emissions associated with machinery manufacture and use. Although soil OM losses from cultivation and cropping will vary widely with soil type, use of no till cropping may lower annual average losses (Reicosky et al., 1995; Maljanen et al., 2007) to only 1000 kg/ha, resulting in emissions of about 0.4 kg CO2 -e/kg wheat. If these values were applied to barley grain fed to fattening beef (Beauchemin et al., 2010), emissions would increase by 1.5–2 kg CO2 -e/kg carcass. Losses of soil OM are well documented and usually detrimental to soil quality and crop production, but OM can be replenished and there is provision in the Kyoto protocol for soil C sinks to be used to offset GHG emissions from grazing. Farming systems evolve to optimise profitability, and some environments are better suited to cropping than grazing, especially where over-grazing could destroy an ecosystem. GHG emissions are only one aspect of the balance between food production and environmental conservation and, although it may appear unreasonable to attribute a lower CH4 yield from enteric digestion to ruminants fed high grain diets without considering all production factors, an alternative approach would be to reward operations for producing ruminant products from pasture systems. Grazing does not require machinery to cut, dry, transport and feed animals, but the best evaluation of ruminant systems should be based on a life cycle analysis incorporating all inputs. With regard to feeding human edible foods to ruminants, Gill et al. (2010) have calculated efficiencies of systems used for providing human edible animal products (i.e., meat and milk), and showed the energetic efficiency of converting animal feed (i.e., forages and concentrates) into milk, beef, pork and poultry meat was about 0.25, 0.06, 0.21 and 0.20, respectively. Further assessment of the efficiency of dietary protein use for protein production in these same products were estimated at 0.20, 0.07, 0.17 and 0.33, respectively. Efficiencies were always lowest for beef production, but capture of energy in milk was similar to that for pork and poultry production. These data were based on systems in the USA and South Korea, with values being similar between countries. However, when the calculations assessed livestock production systems based on their ability to convert human inedible feeds into to human edible products, ruminants were 2–3-fold more efficient than pigs and poultry in the USA (Table 5) and 10 times more efficient than pigs and poultry in South Korea. The difference between countries was a consequence of lower grain inputs to ruminant diets in South Korea. Presently, there are no incentives to separate components of ruminant diets into human edible and inedible fractions, but if global demand for food begins to exceed supply, ruminants would retain their value by producing high quality foods from human inedible forages, albeit with a higher Ei than current values. 5. CH4 yield – variation and measurement In any set of CH4 production measurements, individuals vary in their CH4 yield (Blaxter and Clapperton, 1965; Sauvant and Giger-Reverdin, 2009; Yan et al., 2010). Variation indicates potential opportunities for animal selection, provided the differences are real, persistent and heritable. It appears that differences among some individuals in CH4 yield persist over ˜ et al., 2003; Vlaming et al., 2008) and understanding these differences time (Robertson and Waghorn, 2002; Pinares-Patino could provide further opportunities to mitigate impacts of ruminant animal production systems on GHG emissions. However, some variation in CH4 yield has been attributed to the technique used for CH4 measurement, especially the SF6 technique ˜ et al., 2008), leading to the conclusion that the extent of variation among individuals may be less than (Pinares-Patino previously reported (Hammond et al., 2009). Thus, more research is needed to quantify individual animal variance in CH4 yield using accurate methodology to enable heritability to be estimated in a manner that could be employed for animal selection to lower GHG emissions. Components of rumen function affecting CH4 yield include level of intake, diet composition, products produced in the rumen and the extent of digestion and possible anti-methanogenic comounds in the diet such as condensed tannins (Grainger et al., 2009). Most factors are interrelated. For example, high feed intakes will result in high CH4 production, but the reduced rumen residence time associated with high intake will tend to lower CH4 yield per unit DM intake (Hegarty, 2004). The decline in CH4 yield with increasing intake above ME maintenance applies to both fresh pasture (Muetzel et al., 2009) and concentrate diets (Yan et al., 2010). In a meta-analysis of several hundred measurements derived from calorimetry experiments, Sauvant and Giger-Reverdin (2009) showed the decline in CH4 (g/kg digestible OM (DOM) intake) for multiples of ME maintenance intake (M) to be: CH4 = 33.4 − 4.10 × M. Average values were 26 ± 5.5 g/kg DOM and average OM digestibility was 66 ± 7.5%. Methane yield with increasing proportions of concentrate in the DM decreased curvilinearly with effects being biggest when intakes exceeded twice ME maintenance requirements. These findings, based on data from sheep, cattle and goats, support conclusions of Blaxter and Clapperton (1965) that decreases in yield with increasing intakes were largest with highly digestible diets, and suggest the reductions in CH4 yield from selection of efficient animals (i.e., low RFI) will be biggest when they have high intakes and are most productive. Possibly the biggest challenge in measuring CH4 yield in individuals is to ensure that data are valid, which requires accurate measurement of CH4 production and feed intake. If either CH4 or intakes are measured indirectly (e.g., using markers) they may not be sufficiently precise to distinguish small differences among individuals. For example, the SF6 technique has been shown to account for substantial variation among animals, although the mean values for CH4 yield calculated using this technique (Clark et al., 2006) are similar to those calculated by calorimetry (Hammond et al., 2009). There is a small ˜ and Clark, 2008), but SF6 significant correlation between rate of SF6 marker release and CH4 production (Pinares-Patino does not measure losses of CH4 via flatus. Comparisons of CH4 yield measured by SF6 and calorimetry suggest SF6 values are about 8% lower than chamber values (Grainger et al., 2007).

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Additional concerns about the SF6 technique arise from comparisons of CH4 yield from sheep fed ryegrass with those fed either white clover or chicory (Chicorium intybus). Previous measurements using SF6 (Waghorn and Woodward, 2006), showing low CH4 yield from sheep fed chicory (16.2 g/kg DM) or white clover (12–16 g/kg DM) were not confirmed by calorimetry. Both Sun et al. (2011) and Hammond et al. (2011) have reported similar CH4 yield from fresh ryegrass, white clover and chicory (∼22 g CH4 /kg DM intake), meaning that the accuracy of the SF6 technique may be affected by feed type. However, the similar CH4 yield from these three forages is logical, because similar digestion of them suggest similar H2 release, and consequently CH4 production. Accurate estimates of feed intake of ruminants confined to pens can be relatively easily obtained using electronic feed intake systems, whereas use of markers (e.g., alkanes, Cr, indigestible fibre) to measure forage intakes of individual grazing animals is generally inaccurate. In theory, the ‘alkane’ technique is an ideal method for determining intakes of individuals, but with pastures containing many forage species, variability in the concentrations of alkanes in forage consumed and poor faecal recovery renders this technique unreliable. Use of indigestible markers to determine intakes of grazing animals assumes a similar digestibility of forage among individuals, an assumption that is unlikely to be true, and may be responsible for some of the divergence in RFI. In some reports of CH4 yield, animal intakes have been based on values from feeding tables, ignoring variation in RFI among individuals. Furthermore, values from feeding tables overestimate true intakes if the gas sampling apparatus employed in the SF6 technique interferes with grazing and reduces voluntary intake. This is especially true with young animals, where actual intakes will be less than predicted, and calculated CH4 yields will be low. Inaccurate estimates of intake from grazing ruminants, together with uncertainties concerning CH4 measurement by SF6 , demonstrate a need for considerable care when using these data to estimate CH4 yield, and the validity of some published data is questionable. An exception may be the high release SF6 capsules (170 mg/d) used by Hegarty et al. (2007), but these have not yet been compared with calorimetry. Methane yields from individual animals based on SF6 and indirect or calculated intakes are not defensible and should not be used to compare individuals. Methane production, or CH4 yield, of individuals selected for either low RFI, or in response to supplementation with mitigants such as tannins, oils or saponins (Beauchemin et al., 2008) need to be evaluated in respiration calorimeters. This technology also poses challenges because animals need to be confined for 2–3 d, and feed intakes often do not reflect those during normal activity. Additional problems occur when fresh forages are offered, as cut forages do not offer the same opportunities for animal selection as occurs during grazing. 6. Systems efficiency Ruminant meat production systems have a high Ei relative to pig and poultry production (DEFRA, 2008; Table 5), and ruminant production must strive to reduce Ei in order to expand its contribution to the global food supply in an emerging global C economy. Ruminant farming system models have examined opportunities for limiting emissions while maintaining constant or increased production of beef (Charmley et al., 2008; Hunter and Neithe, 2009), dairy (Lovett et al., 2006; Beukes et al., 2010) and sheep (Alcock and Hegarty, 2006; Cruickshank et al., 2009). These papers address the impact of changing many of the variables in Table 1 (e.g., reproduction, feed quality, potential longevity) on emissions, profit and product output. The common message derived from assessments is that a suite of management options can increase feed efficiency and reduce the Ei of ruminant enterprises by reducing the proportion of feed energy expended on animal maintenance and increasing the proportion expended on production. These options include increasing reproductive rate (Bentley et al., 2008), growth rate (Hunter and Neithe, 2009) or milk production of ruminants (Beukes et al., 2010) as well as selection for RFI. While genetic improvement is a key tool to improving these aspects of performance, superior genetics must be combined with a level of nutrition adequate to support these high performance genetics. For example, poor nutrition will cause extended post-partum anoestrus in cows of high genetic merit for milk, necessitating more replacement heifers and thus increasing herd maintenance feed needs and reducing overall feed use efficiency (Lovett et al., 2006). While management changes can reduce the amount of feed required to meet a desired production target, their impact on total CH4 emissions arising from an enterprise depends on the farmer’s response to having ‘spare’ feed available as a result of more feed efficient livestock. Increasing animal numbers in order to continue to use all available feed is option, but another is to keep animal numbers unchanged, and not graze the ‘spare’ pasture but harvest it or utilise the land for conserved feed production or non-animal uses. When ‘spare feed’ arising from improved feed use efficiency is utilised by increased stock numbers, emissions will increase, even though Ei will be reduced (Alcock and Hegarty, 2011). When additional feed is provided to enable still higher production and further reduce the proportion of feed energy used in maintenance, reductions in Ei of lambs can exceed 50%, but Ei reduction across the flock will be less than 5% due to the ewe being the major emitter of CH4 in the production cycle (Alcock and Hegarty, 2006; Alcock et al., 2008; Cruickshank et al., 2009). Assuming moderate rates of adoption by farmers and moderate selection pressure, Alford et al. (2006) demonstrated that selection for improved RFI will reduce CH4 emissions from the Australian beef herd by 568 kt over 25 years. This assumes ‘spare feed’ is not utilised and animal numbers remain unchanged. It may be expected that graziers would often increase stocking rate to utilise spare feed and, in this case, having animals with an RFI that is 10% superior than normal would only reduce total flock emissions by <6% (Alcock and Hegarty, 2011). Relatively simple changes in farm management, such as supplementary feeding practices, which increase productivity of individuals will generally reduce Ei, but they are generally of less benefit when calculated across the flock/herd than for slaughter or milking animals. However, by reducing feed utilised for animal maintenance, and opting to maintain an equal or reduced number of more productive animals, enterprises can maintain product output with reduced total emissions.

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If however, farm enterprises choose to increase animal numbers to use ‘spare’ feed resulting from more efficient animals, substantial increases in product output will be associated with lower Ei, but with increased total emissions. This is especially the case if increased N fertiliser is used to achieve higher pasture DM production (Bassett-Mens et al., 2009). 7. Conclusions Greenhouse gas emissions from ruminant animal production systems will be lowered by selecting individuals with a low RFI because they require less feed than the average for the same level of production. Most published studies do not suggest differences in CH4 yield for animals with divergent RFI, with lower emissions being a consequence of lower intakes. Animals selected for low RFI can be used in both intensive and extensive farming, and should complement selection for improved health, productivity and profitability. However, individuals selected for RFI in one environment might not exhibit the trait in another, as shown by the failure of Holstein cows selected under a total mixed ration regimen to adapt to competitive grazing (Macdonald et al., 2008). It is essential that selection for low RFI and/or low CH4 yield, is not associated with weaknesses in animal functional traits, such as susceptibility to disease or inferior reproduction, because animal health and fertility are integral to efficient animal production systems. Emissions intensity is a useful measure to assess livestock production systems from a GHG emissions perspective, especially when they are based on life cycle analysis because the real value of ruminants as convertors of forage into high quality human foods will be realised. Conflict of interest statement None. References Alcock, D., Hegarty, R.S., 2006. Effects of pasture improvement on productivity, gross margin and methane emissions of a grazing sheep enterprise. Int. Congr. 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