ANIMALCHANGE SEVENTH FRAMEWORK PROGRAMME

THEME 2: FOOD, AGRICULTURE AND FISHERIES, AND BIOTECHNOLOGIES

Grant agreement number: FP7- 266018

DELIVERABLE 6.1

Deliverable title: The likely decreases in GHG emissions that can be obtained through improvements in animal genetics

Abstract: Altering selection objectives in ruminant breeding programs to target environmental goals could enhance the reduction in GHG emissions at a relatively small economic cost. Genetic improvement tools provide a useful and cost-effective mechanism to help livestock agriculture meet the challenges of the reducing GHG emissions.

Due date of deliverable: February 2013

Actual submission date: March 2014

Start date of the project: March 1st, 2011 Duration: 24 months

Organisation name of lead contractor: SRUC, Eileen Wall

Revision: V1

Dissemination level: PU

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Content

1. BACKGROUND 2

2. HOW CAN GENETIC IMPROVEMENT IN RUMINANTS REDUCE GHG EMISSIONS?

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3. DEVELOPING NEW TRAITS TO BE INCLUDED IN BROADER BREEDING GOALS 4

4. ARE METHANE EMISSIONS FROM RUMINANTS UNDER GENETIC CONTROL? 5

5. ASSOCIATION BETWEEN RFI AND GHG EMISSIONS 7

6. THE EFFECT OF THE HOST (ANIMAL) ON THE POPULATION OF

METHANOGENIC MICROBES IN THE GASTROINTESTINAL TRACT 8

7. EFFECT OF GENETICS ON DIGESTIVE FUNCTION 9

MODELLING THE IMPACT OF GENETIC IMPROVEMENT ON GHG EMISSIONS FROM BEEF, SHEEP AND DAIRY

SYSTEMS ............................................................................................................................................... 9

Methods .......................................................................................................................................................... 9

Impact of incorporating GHG emissions in ruminant breeding goals .................................................... 10

Impact of selecting on breeding goals to reduce GHG emissions .......................................................... 10

8. CONCLUSION 14

ACKNOWLEDGEMENTS 15

REFERENCES 16

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Review of the role of genetic improvement in reducing greenhouse gas emissions in livestock

1. Background

Livestock production accounts for 70% of the agricultural land on the planet. Given that demand for livestock products is expected to double by 2050 it is vital that we identify less polluting ways of producing more food, spanning both intensive and extensive systems. Northern Europe is one of the few parts of the globe where climate change is expected to be neutral or even benefit agricultural productivity. Hence it is likely to make an even more important contribution to global food supply in the future. Much of the land area currently used for livestock production is unsuitable for cropping due to topography, soil type etc. Additionally conversion of such land to cropping may well have adverse effects on carbon balance.

Many non-genetic farm technologies require ongoing investment of some sort to maintain the commercial benefit (e.g., dietary manipulation to improve fatty acid composition of milk). However, genetic improvement of a livestock population is effectively a permanent change and does not require additional or continuing resources. Genetic improvement can play an important role in developing ruminant systems that will be sustainable in the future, and produce food in an environmentally friendly manner. Also, genetic improvement of livestock is a particularly cost-effective technology, producing permanent and cumulative changes in performance. Ruminant genetic improvement has been shown to be a cost-effective mechanism of reducing GHG emission in ruminant production systems. Improving adoption, and continued development, of ruminant genetic improvement tools will help farmers be proactive and reach, and potentially exceed emissions targets. This review focuses on the impact of manipulating the genetic components of the relevant traits in ruminants that affect GHG emissions directly or indirectly through production efficiency.

2. How can genetic improvement in ruminants reduce GHG emissions?

Many production and fitness traits have been shown to have a genetic component and have scope to be improved via genetic selection. Many ruminant breeding goals around the world select on both production and fitness traits have can help to mitigate GHGs from many livestock systems as some examples below demonstrate. Selection for efficiency of production in livestock species will help to reduce emissions. In many cases this can be achieved simply through selection on production traits.

Reducing the number of animals required to produce a fixed level of output: There has been an overall reduction of annual methane emissions (28% from 1990 to 1999) in the UK. The reduction in methane emissions from agriculture in the same period has been low (4%) and can mainly be attributed to a decrease in cattle numbers due to increased productivity in dairy cows (Defra, 2001). The dairy sector in Canada has reduced its methane emissions by 10% since 1990 also by reducing the number of animals (Désilets, 2006).

Increasing the efficiency of production will help reduce the finishing period for meat animals, therefore reducing emissions per unit output. Hyslop (2003) demonstrated that efficiency of the beef production system was paramount in reducing the GHG emissions/unit output showing that intensive concentrate based systems produce the lowest emissions (note: this

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study did not consider the externalities of the system such as the carbon cost of producing concentrate diets). Further analyses of the data showed that there was also a significant breed difference suggesting that bigger continental breeds of cattle produced less emissions/unit output than the smaller British type breeds (Hyslop, 2003).

Due to the high feeding costs associated with beef production systems, improving the efficiency of feed utilisation (or efficiency) complex will have a great effect of the global efficiency of the system. Thompson and Barlow (1986) showed that greater improvements in enterprise efficiency would result from an improvement in feed conversion efficiency of the growing animal and reduction in feed intake of the breeding herd. In addition, increasing feed efficiency may improve not only production efficiency but also reduce the GHG emissions because of the association between the level of methane production rate per day and per animal and the level of food intake (Nkarumah et al., 2006). Although feed intake is the main trait associated with feeding costs, it is not selected for in breeding programs because of the strong correlations with the output traits (Archer et al., 1999). Therefore, in isolation, it is not informative about feeding or production efficiency.

Feed conversion ratio and residual feed intake are the main indicators to measure the efficiency of the conversion of food into product. Both indicators are the result of combining output traits and feed intake. Feed conversion ratio (FCR) is given by the ratio between output trait and feed inputs. Examples relevant for the beef production systems are the efficiency of growth and/or lean growth, which are computed as the ratios between total body growth or lean growth and feed intake over a defined period of time. Residual (or net) feed intake (RFI) is calculated as the difference between actual and predicted feed intake based on “standard requirements” for production and live weight maintenance. Residual feed is the feed that cannot be accounted for any of these purposes. Therefore, differences in RFI are related to the ability of the animal to be more efficient and consume less feed for the same output (van der Werf, 2004). Requirements can be calculated from the phenotypic regression of feed intake on production and maintenance (Archer et al. (1997) or form feed tables or nutritional models (Garrick, 2006).

Review of heritability estimates for feed efficiency traits have been produced over the years including Archer et al. (1999), Herd et al. (2003) and Berry and Crowley (2013). In the latest of these reviews meta-analyses showed that the feed efficiency complex was heritable with a pooled heritability for RFI and feed conversion efficiency in growing cattle of 0.33 ± 0.01 (range of 0.07 to 0.62) and 0.23 ± 0.01 (range of 0.06 to 0.46), respectively and a heritability in cows for gross feed efficiency and RFI of 0.06 ± 0.010 and 0.04 ± 0.008, respectively.

Selection for fitness traits (lifespan, health, fertility) will help to reduce emissions by reducing wastage of animals. For example. improving lifespan in dairy cows and maternal line animals (i.e. ewes and beef cows) will reduce wastage by reducing the number of followers. For example by improving lifespan in dairy cows from 3.02 to 3.5 lactations will reduce methane emissions by 3%.

Improving health and fertility will reduce involuntary culling rates. This reduces emissions from dairy systems and beef and sheep systems (increased maternal survival) by reducing the numbers of followers required. Improving fertility will reduce calving/lambing intervals and inseminations resulting in shorter dry/unproductive periods. This reduces management costs as well as emissions. Improving health reduces incidence of health problems/diseases, thereby improving animal welfare and reducing treatment costs (and lower antibiotic use) and reducing emissions by maintaining the productivity level of the animal (which is reduced during periods of poor health). Garnsworthy (2004) estimated, using modelling, that if cow fertility was restored from the level in 2003 to the level in 1995 that methane emissions from the dairy industry would reduce by 10-15%.

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Improving calving and maternal traits will reduce emissions by improving survival of offspring during the peri-, neo- and post- natal periods. This will reduce wastage in a farming system, thereby decreasing overall emissions as well as improving calf and dam welfare and survival.

3. Developing new traits to be included in broader breeding goals

Feed utilisation efficiency in ruminants: Feed utilisation has been considered in the selection programmes for pig and poultry species. Due to the nature of many ruminant production systems, with less opportunity for intensive feed recording, the use of such traits in selection has been limited but there have been some examples. Herd et al. (2002) showed that there is a decreased enteric methane production per day in animals selected for reduced residual feed intake. Reduced residual feed intake is akin to selection for high feed efficiency as an animal is eating less but maintaining a similar growth rate (high net feed efficiency) and therefore less feed is required to produce a unit of output. Lines were divergently selected for high and low residual feed intake and showed no significant differences for most production traits. This shows the possibilities for selection of reduced GHG emissions through the selection of animals which use less feed and produce less methane than average to achieve a given level of performance.

The use of FCR in breeding programmes as the efficiency trait has been limited due to some disadvantages. On one hand, FCR is highly correlated with output traits, which implies that it is not independent of the level of production. In addition, some of these correlations may be unfavourable. The selection to reduce FCR and thus improve efficiency would be accompanied by an increase in growth rate, and an increase in mature cow size (Koots et al.,1994). On the other hand, FCR has a second disadvantage that relates to problems inherent with selection on ratio measurements. Traits defined as ratios can cause problems when used in linear selection indices, especially when one of the component traits is in the index because the joint distribution is non-normal and invalidates the selection response based on linear indices (Gunsett, 1984). The main advantage of using RFI as an efficiency trait instead of FCR, as stated in the literature by Archer et al. (1997) and Herd and Bishop (2000), is the fact that RFI is not defined as a ratio trait and has favourable association with other economically relevant traits.

Traits related to the efficiency of absorption of dietary nutrients: Direct selection for efficiency of utilisation of the different components of the diet is difficult to achieve as many animal and feed parameters need to be collected. Work on these types of traits has mainly been at an experimental level. Ferris et al. (1999) showed that medium genetic merit (for production) Holstein-Friesian cows have higher nitrogen and methane emissions per unit of N and gross energy intake respectively than high genetic merit cows. This suggests that high genetic merit cows convert the energy and protein components of the feed more effectively than medium genetic merit cows. Hegarty (2004) reviewed the evidence for a genetic difference in gut function in ruminants covering genetic components of things such as diet selection and eating rate, digestive kinetics and methane production. There were data to suggest that there are genetic differences in the amount of methane produced/unit feed intake. Further examination of the metabolic turnover of nutrients in animals may be required to understand the underlying biological differences between high producing animals in their feed utilisation and lower genetic merit animals. Other metabolic traits that require further examination due to their impact on emissions include water dynamics (manure consistency), gut function (nutrient and mineral absorption) and litter quality.

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4. Are methane emissions from ruminants under genetic control?

Methane emissions from ruminant animals are a particularly important contributor to global GHG emissions. Therefore it is important to investigate whether evidence exists for genetic variation of methane in ruminants. If this does exist then this provides a useful opportunity to mitigate GHG is by exploiting between-animal variation in emissions, by directly selecting animals for lower yields of GHG. This would provide a relatively inexpensive method to produce a long-term reduction in GHG emissions (Vlaming et al. 2008).

From a review of the literature it is clear that a lot of effort has focussed on investigating the relationship between nutrition and methane production where the main factors affecting methane production are the level of intake and composition of the diet (Hegarty 2009). Furthermore, there has been a lot of investigation into the use of different compounds to inhibit methanogens in the rumen, such as ionophores, antibiotics and methane analogues (Hegarty 2009). In contrast, the relationship between genetics and methane production has been less extensively studied, but experimental activity is increasing globally.

Evidence of between-animal variation in methane emissions

Research in the literature indicates that large between-animal variation in methane emissions exists in ruminants, however, the evidence is relatively limited, most probably due to the difficulty in collecting a suitable dataset and in particular obtaining accurate measurements of methane emissions that are repeatable over time. Due to the limited evidence available in beef cattle, this review will cover information available from other animal species as well as humans.

Reports in the literature show conflicting evidence for between-animal variation in methane emissions. Within- and between-animal variation in methane emissions has been reported from studies using respiration chambers as well as the SF6 tracer technique. As early as 1965, Blaxter and Clapperton reported both within- and between-animal variation with coefficients of variation (CV) of 7% within-animal in cattle and sheep and 7-8% between-animal in sheep, using measurements from a closed-circuit respiration chamber.

Using the SF6 technique Lassey et al. (1997) obtained measurements of methane from grazing sheep (n=50) and lactating dairy cows (n=10) in New Zealand. They reported large inter-sheep variability in daily methane emission that could not be attributable to variable intake. They reported that this suggested an appreciable diversity of methanogenetic response to digestion, and may be significant in the search for strategies to control emissions of this greenhouse gas.

Boadi and Wittenberg (2002) investigated methane emissions from six dairy (Holstein) and six beef (Charolais x Simmental) heifers. In this study measures of methane emissions were obtained with the SF6 tracer gas technique. This study found large within- and between-animal variation in methane emissions with CV of 26.9 and 26.6%, respectively.

Pinares-Patino et al. (2003) monitored methane emissions from four low and four high methane emitters, selected from a flock of 20 Romney sheep on the basis of methane production rates per unit of intake measured at grazing using the SF6 tracer technique. Methane emissions were monitored at grazing for four periods. In this study, low methane emitters were heavier than high emitters in all periods but they did not differ in their gross energy intakes. Low and high emitters constantly maintained their initial rankings in methane yield (%) throughout the subsequent periods and the correlation analysis of rank order for

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methane yield showed strong between-period correlation coefficients, although this was weaker in the last period. It is suggested that feeding conditions that maximize feed intake favour the expression and persistence of between-sheep differences in methane yield. In this study sheep were maintained on same diet. Grainger et al. (2007) investigated methane emissions in Holstein-Freisian dairy cows (n=16) using both open-circuit respiration chambers and the SF6 tracer technique. This study found that methane emissions were similar with both techniques. A higher variability within cow between days was found for the SF6 tracer technique (CV = 6.1%) than for the chamber technique (CV = 4.3%). The variability among cows was substantially higher than within cows and was higher for the SF6 technique (CV = 19.6%) than for the chamber technique (CV = 17.8%).

More recently, Vlaming et al. (2008) investigated four non-lactating dairy cattle to assess within- and between-animal variation in methane emissions over time measured using the SF6 technique. This research confirmed the work of others which showed considerable within- and between-animal variation in methane production from animals receiving the same diet. However, the within-animal values were higher (7-10%) using SF6 that in studies using calorimetry (4-7%).

Furthermore, research by Robertson and Waghorn (2002) has reported an effect of genotype on the methane production of cows. Methane production was investigated in two genotypes of cows: the New Zealand Friesian and Dutch/US Holsteins. This study showed that cows of the Dutch/US Holstein genotype produced 8-11% less methane as a % of gross energy in comparison to the New Zealand Friesian cows. The results of this research suggest a genetic component responsible for variation in methane emissions. This therefore supports the possibility of directly selecting animals for lower methane emissions based on the animal genotype.

Pinares-Patino et al. (2000), investigated between sheep differences in methane emissions of Romney Sheep over a long term (more than 1 year). Five low and five high emitters were selected based on measurements using the SF6 tracer technique. Further measures were taken, by calorimetry chambers in the first instance followed by the SF6 tracer technique in the second. In this study they found no persistence in methane emissions of these animals over the long term. An important point to note is that the feeding conditions of these animals changed throughout the trial which is an important factor which influences emissions.

Goopy and Hegarty (2004) investigated eight Angus steers whose methane emissions have been found to be higher or lower than predicted when fed a commercial feedlot diet. These were re-tested on a medium quality forage diet. From this study they found that differences in actual versus predicted emissions were diminished and several animals changed in ranking, therefore they found that high and low methane emitters was not maintained across diet types. This is similar to the study of Pinares-Patino et al. (2000), already outlined in sheep. Pinares-Patino et al. (2003) also found that ranking of sheep based on emissions changed with alterations in the composition of diets. This indicates that any selection for low methane emissions will need to be diet specific.

Using measurements obtained with open circuit respiration chambers, Munger and Kreuzer (2008) investigated methane emissions of three different breeds dairy cows (Holstein, Simmental and Jersey, n=10 each) to test the assumption that there are genetically low methane producing animals. From this study they found a lack of persistence of individual animal differences in methane emissions and suggested that genetic determination of this trait is of minor importance in dairy cows.

Pinares-Patino et al. (2008b) investigated methane emissions in cattle which were divergently selected for bloat susceptibility. Bloat susceptibility is a genetically inherited trait and they wanted to explore whether cattle divergently selected for bloat susceptibility also

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differed in their methane emissions. They found that low and high bloat susceptible genotypes did not differ in their methane yields per unit of feed intake. If there had been differences then animal breeding might have had 2-fold consequences: low bloat susceptibility and low methane. This research does not indicate that genetics does not influence methane emissions, only that bloat susceptibility doesn’t seem to influence methane emissions.

There are a few points to note which may explain the lack of persistence of methane emissions in these studies. Firstly, these studies have generally used low sample sizes that are inaccurate representations of the population and present low statistical power. Further, achieving accurate and repeatable measures of methane emissions is a difficult task and therefore the lack of persistence in methane emissions described here may be influenced by the measurement techniques. This strongly indicates a requirement for obtaining large sample sizes with accurate and repeatable measures of methane emissions. Furthermore, diet variability within and between experiments will affect methane emissions, therefore it is essential to feed a highly standardized diet in these types of experiments to limit variation in methane emissions.

On the whole, evidence in the literature suggests that there is between-animal variation in methane emissions, therefore, breeding directly for lower emissions is feasible and may be an important tool for the mitigation of methane emissions from ruminant livestock.

5. Association between RFI and GHG emissions

Methane production is dependent upon the quantity of feed consumed, although this effect is moderated by feed digestibility and other feed and animal characteristics, such as composition of the diet and dietary fat (Hegarty, 2009). The feeding efficiency of the animal may also have an effect with, low-RFI beef cattle eating less than expected for their live weight and growth rate. Angus cattle divergently selected for RFI currently attain the same growth rates but differ by approximately 15% in their voluntary feed intake (Herd et al., 2002). Consequently, lower volume of methane would be produced for more efficient animals. Cattle selected for low RFI can therefore be expected to produce less methane than do high RFI cattle (Herd et al., 2002).

By comparing the methane emissions of 66 Angus steers chosen from breeding lines divergently selected for RFI, Hegarty et al. (2007) found that the most efficient animals (low RFI) had lower methane production rate. The authors concluded that the use of low-RFI sire will have an effect on the total methane emissions, although RFI explained a low proportion of the variation of methane production rate (Hegarty et al., 2007). The association between RFI and methane emission was also evident in comparison between 306 efficient and inefficient cattle in a feedlot test reported by Nkrumah at al. (2006). Methane production was 28% less in low-RFI animals compared to high-RFI animals (Nkrumah at al., 2006).

An important aspect to take into account is that the greatest abatement from selection for RFI would be achieved on low digestibility diets such as pastures, given the effect of digestibility and the large proportion of enteric methane attributable to grazing livestock (Hegarty et al., 2007; Alford et al., 2006). Alford et al. (2006) suggested that the relationship between methane production rate and RFI-EBVs should be defined in ruminants grazing low and moderate digestibility pasture, as well as the in feedlot diets considered in most of the studies. Selection for low RFI may also have a favourable effect on the total amount of manure produced and also on the potential quantity of nitrous oxide liberated from these manures. This potential effect on nitrous oxide can be due to a reduction in the total N intake and a greater efficiency of capturing the dietary N because low RFI animals accrete body

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tissue t the same rate than high RFI animals an may have reduced protein turnover (Hegarty et al., 2007).

6. The effect of the host (animal) on the population of methanogenic microbes in the gastrointestinal tract

To date little is known about the influence of the host on the population of microbes in the gastrointestinal tract. It may be that there is a genetic component which influences the microbial community in the gut and therefore affects production of GHG. Understanding the hosts influence on the population of microbes in the gastrointestinal tract is therefore important for mitigating GHG.

Interactions between the host and the microbial community are very complex and not well understood (Shi et al. 2008). Little is known about the association between specific ruminal microbial population and the efficiency with which feed is converted into energy for the maintenance and growth of the host (Guan et al. 2008). According to Shi et al. (2008), various studies have suggested that host specificity of a faecal bacterial community is a general phenomenon that is not restricted to one animal species.

There is evidence in the literature to suggest that the host does influence the microbial population of the digestive tract. Research has shown that there is variation in the rumen microbial population among animals even if fed a highly standardized diet. Hackstein et al. (1996) reported a genetic link from reviewing methanogen-host relationships and concluded that the presence of methanogens is under genetic control rather than dietary.

Shi et al. (2008) carried out a study to determine whether the host species determines the microbial community structure in the goat rumen using three goat species. Shi et al. (2008) reported that the bacterial community was influenced differentially by the different goat species. Such differences may reflect the influence of individual goats on the rumen bacterial community. Interspecies variations in bacterial populations were noticeably greater than intraspecies variations, indicating that the bacterial community in goat rumen is species specific. They reported that the ciliate community structure of the rumen was not species specific as there was considerable intraspecies variation. They concluded that host-species related factors have an important influence on the bacteria, but not ciliate, composition in the gut rumen.

Due to the importance of the gut microbial population in health and disease of humans, there has been interest in investigating the factors which influence this, including genotype (Zoetendal et al. 2006). Several studies have shown that the composition of the bacterial community is host-specific and stable over time (Zoetendal et al. 1998; Tannock et al. 2000; Seksik et al. 2003; Vanhoutte et al. 2004)

Guan et al. (2008) hypothesized that the diversity of ruminal micro-organisms and the concentration of volatile fatty acids, one of the fermentation parameters in the rumen, are associated with the feed efficiency (RFI) of cattle. Guan et al. (2008) reported potential associations between rumen microbiota and feed efficiency of cattle.

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7. Effect of genetics on digestive function

The variation observed between animals may be due to factors associated with digestive function including retention time of digesta, ruminal capacity, pH, microbial population and saliva concentration and secretion. An important factor which influences the microbial population of the gastro intestinal tract is the mean retention time or rumen retention time of digesta. Variations in the mean retention time (and rumen retention time) could be an important factor which influences differences in fermentation in the gastrointestinal tract of humans and livestock (Hegarty 2004). For example, it may induce changes in the ruminal microbe species (bacteria, protozoa, and methanogens), volatile fatty acid pattern, chemical composition of the microbes and so the energetic efficiency of the growth of microbes themselves. Further consequences of differing mean retention time and rumen retention time, such as decreases in methane production or increased microbial long chain fatty acid synthesis may also be important in affecting the energetic efficiency of diverse animal genotypes (Hegarty 2004).

Modelling the impact of genetic improvement on GHG emissions from beef,

sheep and dairy systems

Methods

This next section briefly describes the results of a modelling exercise that examined the impact of selecting on reduced GHG emissions in the context of broader breeding goals. We are focusing on UK breeding goals for dairy, beef and sheep scenarios. The study of Stott et al. (2005) described how relative economic values (REVs) are calculated for traits included in the UK dairy profit index (£PLI) using dynamic programming tools to model a whole farm system. The selection indices available for sheep in the UK encompass a variety of traits depending on the type of sheep, placing greater emphasis on the traits that are of specific interest. The terminal sire index focuses on growth and carcass quality with the aim of increasing lean tissue growth rate while resulting in little or no increase in fatness (Simm and Dingwall, 1989) while hill type breeds incorporate fertility and maternal traits (e.g. litter size, maternal eight week weight, mature size, longevity) along with lamb growth and carcass traits (Conington et al., 2001, 2004, 2006a). The national beef indices have evolved with time, reappraising the economic weights for production based traits (e.g, beef carcass traits, Amer et al., 1998) to the addition of new traits to broaden the overall breeding goal (e.g., maternal traits, Roughsedge at el., 2005). In all of these beef, sheep and dairy scenarios the REV for each trait is calculated by examining the consequence of a unit change in a trait of interest on net farm revenue, while keeping all other traits in the index fixed.

Wall et al. (2010a) described how whole farm model of greenhouse gas (GHG) emissions could be developed to help investigate the potential to use genetic selection as a tool to reduce ruminant GHG. This study utilised IPCC (2006) Tier II/III methodology were used as the basis for developing the GHG models. The GHG benefit/cost of trait changes was estimated by first posing a ‘base scenario’. The ‘base scenario’ had typical production and performance values for that farm type as described by the respective economic models for the beef, sheep and dairy scenarios from which REVs were estimated. Once the base system for each scenario was set the individual traits were altered, one by one, holding other traits constant. The impact of changing the biological traits on GHG emissions from the animals (e.g., CH4 from enteric fermentation, N2O from animals grazing) and from the management of the manure were calculated. Traits (e.g., milk yield and cow fertility) were altered separately and the total farm GHG were estimated to compare to the ‘base scenario’.

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Where appropriate changes in ruminant feed requirements were calculated based on the genetic response seen in a trait. The feed requirements had a carbon benefit/cost attached to them depending on whether the trait lead to lower or higher energy requirements for the whole farm system respectively. The reasoning for this was if a trait change lead to a higher total energy requirement, the farmer would have to buy in supplementary feed or produce more feed at a GHG cost. Alternatively, a reduction in feed requirement could enable a farmer to cut back on GHG associated with producing or buying feed (i.e., putting on less fertiliser or buying less concentrates). The carbon cost of the feed was based on data from the Cranfield LCA model. Further details of the results of this approach to quantifying GHG implications of a unit change in traits can be found in the Report to Defra (Wall et al., 2010).

Impact of incorporating GHG emissions in ruminant breeding goals

Until recently, the consequences of selection on various indices have been examined in terms of their biological (e.g., changes in traits) and economic perspective. In the past selection indices have focussed on production traits. However, the genetic correlation estimates between production, health and fertility are predominantly unfavourable and therefore selection indices (and breeding goals) have been updated to include a range of both production and functional (fertility, health, survival traits).

Selection index theory (Hazel, 1943) was used to examine the consequences of selection on current and alternative future breeding goals. Models of selection in dairy were developed to explore the expected responses in the component traits, both those in the breeding goal and index as well as a range of correlated traits, as well as the overall economic and environmental performance of alternative selection indices. For example, the overall breeding goal for the current selection index in dairy cattle, £PLI, is profit, milk, fat and protein kgs, lifespan, mastitis, lameness and fertility as goal traits. This was used as the base index to compare the impact of different breeding goals to. Expected responses to selection of alternative breeding goals, with differing weights were examined by building a selection index framework. Phenotypic and genetic parameters between the traits in the breeding goal, selection index and correlated traits of interest were collated from previous studies and responses to selection on the index were calculated (Hazel, 1943).

The change in overall GHG emissions related to a change in an individual trait can be used to calculate a new set of weights for the alternative ruminant breeding goals. These environmental weights are expressed in 2 forms, in terms of CO2 e per breeding animal and per kg of unit product (i.e., milk or meat). The impact of a unit change in a trait on GHG emissions can be used as stand-alone selection index weightings ("relative environmental values") to create an environmental selection index.

Impact of selecting on breeding goals to reduce GHG emissions

Dairy: The expected annual response in milk yield/cow increased when selection index weights were increase from current economic weights to environmental weights (79kg vs. 116 kg, Table 1). However, the negative weighting on milk fat in the environmental index resulted in a lower rate of improvement in milk solids when selecting on an index with environmental index (3.94 kg/cow/annum improvement in milk fat with current index vs. 2.58 kg with environmental index). Generally the environmental index weights resulted in a poorer response across functional traits compared to current economic index weights. For example, the expected annual response in lifespan with the current index is 0.055 lactation/cow and this response falls to 0.014 lactations with an environmental index. Also, the expected response in condition score, was predicted to get worse when selecting on an environmental index compared to the current index. The expected responses in traits between the two environmental index weights (GHG1 and GHG2) were negligible.

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All of the indices studied resulted in a positive economic response per cow/annum ranging from £3.21 with an environmental index to £7.11 with the current index (Table 2). All of the indices studied also resulted in a favourable response in overall GHG emissions ranging from a reduction of 33.5 kg CO2e/cow/annum with the current index to 64.07 kg CO2e/cow/annum with the environmental index. This equates to a doubling of the expected response in the reduction of GHG emissions from a dairy system when the selection index is altered from the current index to an environmental index. The environmental index is predicted to have a cumulative reduction on GHG emissions of dairy systems by 1% per cow per annum.

Table 1. Index and correlated trait responses for dairy cattle when three different breeding objectives were selected

Breeding objectives and trait responses (in trait units pa.) Current GHG1 GHG2 Milk (kg) 79.29 116.05 116.06 Fat (kg) 3.94 2.58 2.58 Protein (kg) 2.96 2.70 2.70 Lifespan (lactations) 0.055 0.014 0.014 Mastitis (cases) 0.0015 0.0039 0.0039 Lameness (cases) 0.0006 0.0001 0.0001 Calving interval (days) 0.37 0.79 0.79 Non-return rate (0/1) -0.0027 -0.0047 -0.0047 Condition score * -0.021 -0.033 -0.033

* Condition score, 1 = thin, 9 = fat

Table 2. Summary of dairy annual selection response outcomes over the alternative breeding objectives Overall responses per annum Selection index weights used Units Current GHG1 GHG2 Current index £/cow £7.11 £3.21 £3.21 GHG reduction per cow kg CO2e/cow -33.50 -64.07 -64.08 GHG reduction per kg product g CO2e/ kg product -14.15 -28.79 -28.79

Hill Sheep. The current index, in general, had responses in the traits that were positive in direction compared to the indices that were based either on GHG/breeding ewe or GHG/kg lamb (Table 3). However, it should be noted that a positive expected response in all traits may not necessarily be a favourable outcome. For example, the selection on the current index in hill sheep would result in an expected response of 0.028 unit increase in carcass fat score (Cfat) per annum compared to the two GHG index weights which both recorded negative responses. Consumer demand generally stipulates a leaner lamb carcass, making a negative Cfat response beneficial.

Table 4 shows that selecting on an index with environmental weights (GHG1) compared to the current index would result in ewes with a lower mature size (MS) (annual expected response of -1.2 kg and 0.56 respectively) but also with lower weights of lamb weaned (MAT) with a response of -0.11 kg compared to 0.08kg. Selecting on GHG/breeding ewe would also reduce responses in litter sized reared (LSR-D, -0.02%) and 8-week weight (8WK-D, -0.12kg) relative to the current index (0.019% and 0.32 kg respectively).

While expected annual responses in many of the production traits are reduced when selecting on environmental weights compared to economic weights, traits relating to animal health and welfare recorded better correlated responses than the current index. Examples of these include ewe longevity which was 0.006 and 0.04 for GHG1 and GHG2 respectively

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compared to 0.003 for the current index. Selection on a environmental index weights was also predicted to have a favourable response in foot rot score (-0.004 units) compared to the current index (+0.002 units)

Table 3. Index and correlated trait responses for hill sheep for three different breeding goals weights based on current breeding goal (Current), environmental weights expressed per breeding ewe (GHG1) and per kg of lamb meat (GHG2). Breeding objectives and trait responses (in trait units pa.) Current GHG1 GHG2 Carcass Fat (Cfat) 0.028 -0.071 -0.070 Carcass Musclue (Cmus) 0.174 -0.140 -0.135 Mature Size (MS) 0.558 -1.227 -1.200 Maternal traits (MAT) 0.083 -0.114 -0.109 Litter size reared (LSR) 0.019 -0.022 -0.021 8-week weight 0.316 -0.123 -0.119 Residual feed intake (RFI-Lambs) 0.033 0.003 0.005 Residual feed intake (RFI-Ewes) 0.000 0.000 0.000 Ewe longevity 0.003 0.006 0.043 Footrot 0.002 -0.004 -0.004 Lamb survival 0.001 0.001 0.001

Table 4 indicates the calculated overall responses when seven different sets of weights were applied. The current index obtained the highest overall economic response, based on current economic assumptions (i.e. not incorporating carbon prices) with an annual expected response of £0.20 per breeding ewe compared to mass selection. However, this selection on the current index is predicted to have an unfavourable impact on GHG emissions from the system with an expected annual increase of 1.5 kg CO2/ewe, a 0.45% increase over the base scenario.

Selection on an index based on environmental weights (i.e. goal is reduced GHG emissions irrespective of profit) is expected to have an unfavourable effect on overall economic response, with an expected annual response of -£0.08 per ewe when using index weights GHG1. However, selection on the environmental weights (GHG1) is predicted to have a favourable impact on GHG emissions from the system with an expected annual decrease of 4.2 kg CO2/ewe. This represents an expected reduction in emissions per ewe of 1.26% per annum. It is important to note that this expected annual response is cumulative and therefore in 2 years this would represent a 2.5% reduction, and so on.

Selecting on a GHG/kg lamb rather than GHG/breeding ewe basis (GHG2 vs. GHG1) would result in greater reductions in emissions per kg of lamb and higher overall progress on a £/breeding ewe basis. However, the differences between the two GHG indexes are modest.

Table 4. Summary of hill sheep annual selection response outcomes over the three breeding objectives Breeding objectives and overall responses

per annum. Selection index weights used Units Current GHG1 GHG2 Current index £/ewe £0.20 -£0.08 -£0.07 GHG reduction per ewe kg CO2e/ ewe 1.50 -4.18 -4.11 GHG reduction per kg product produced

g CO2e/ kg product

129.91 -376.52 -383.16

Terminal sheep Table 5 shows the expected annual responses in the traits in the selection index with each of the sets of index weights for terminal type sheep breeds. Terminal sheep had fewer profit traits compared to hill sheep as the current index was more focussed on direct growth and carcass composition traits on lambs rather than maternal traits (Table 4.9).

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The two lamb growth traits (8-wk and scanning weight). had higher trait responses when selecting on the environmental indices (GHG1 and GHG2). For instance, selecting on a GHG/breeding ewe index compared to the current index resulted in 0.15kg per annum extra response in 8-week weight compared to selection on the current index. Scanning weight (SWT) was also predicted to have a higher expected response of 0.2kg. In contrast, the environmental index was predicted to have a lower response in lean weight, with an expected annual response of 0.04 kg per annum compared to 0.16 kg per annum with selection on the current index. There is a similar unfavourable change in response with fat weight when selecting on the environmental index compared to the current index, with a predicted response that would reduce fat weight (in the carcass) selecting on the current index but increase it selection on the environmental index. This is important as it concerns the quality of the lamb carcass and consumer preferences for the lamb thus lamb prices.

The current index weights were calculated to result in £0.62 per breeding ewe per annum overall progress (Table 6). Selecting on environmental indices (GHG1 and GHG2) GHG/breeding ewe and GHG/kg lamb indexes reduced overall economic response per ewe to next to zero. However, the environmental index resulted in a larger GHG emissions reduction compared to the current index (0.94 kg CO2e vs. 0.04 kg CO2e respectively), with the environmental index expected to result in a 0.27% cumulative annual reduction in GHG emissions per ewe compared to the base scenario.

Table 5. Index and correlated trait responses for terminal sheep for the three alternative breeding objectives. Breeding objectives and trait responses (in trait units pa.) Current GHG1 GHG2 Trait names Lean wt 0.156 0.044 0.047 Fat wt -0.116 0.072 0.075 RFI-Lambs -0.046 -0.011 -0.009 Lamb Survival 0.001 0.000 0.001 8-week weight 0.038 0.188 0.190 Scanning Weight 0.011 0.210 0.210

Table 6. Summary of terminal sheep annual selection response outcomes over the three breeding objectives Breeding objectives and overall

responses pa. Selection index weights used Units Current GHG1 GHG2 Current index £/ewe £0.62 -£0.01 -£0.01 GHG reduction per ewe kg CO2e/ewe -0.04 -0.94 -0.94 GHG reduction per kg product produced

g CO2e/ kg product

-1.89 -41.81 -41.89

Terminal Beef. As with terminal sheep the current weights and breeding scenario focus on a limited number of production related traits (Table 7). Carcass weight (CW) is one of the largest determinants of farm profitability for a terminal beef scenario. All of the index weightings resulted in a positive and favourable response in CWT, ranging from an expected annual increase of 2.32 kg/annum (current index) to 2.51 kg per annum environmental index, GHG1). However, this had an unfavourable correlated response of functional traits such as gestation length and calving difficulty, both with predicted increasing and unfavourable expected responses across all the index weights. RFI-growing response was -2.7 kg per annum for the current farm profit index compared to -8.7 kg for the environmental index, suggesting that switching to an environmental index will result in a reduction in RFI meaning less feed required to meet a given level of output.

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Table 7. Index and correlated trait responses for terminal beef when three different breeding objectives were selected. Breeding objectives and trait responses (in trait units pa.) Current GHG1 GHG2 Carcass weight (CW) 2.317 2.514 2.504 Carcass Fat Score (CFS) -0.035 0.047 0.048 Carcass Conformation Score (CCS)

0.063 0.033 0.032

Gestation length (GL) 0.016 0.068 0.066 Calving difficulty (CD) 0.005 0.012 0.012 RFI growing animals -2.721 -8.743 -9.006 Shear Force -0.001 -0.001 -0.001 Docility Score 0.009 0.006 0.006

There was a favourable overall economic response with all index weights ranging from £2.84 with the environmental index weight compared to £3.40 with the current index, the overall economic response dropping by only £0.53/cow with a shift from the breeding goal for the current goal, profit, to an environmental goal. At the same time the environmental index had largest impact of GHG emissions reductions, with an annual cumulative response of 0.4% reduction GHG emissions/cow. However, all indices had a favourable impact of GHG emissions from the system.

Table 8. Summary of terminal beef annual selection response outcomes over the three breeding objectives Breeding objectives and overall

responses pa. Selection index weights used Units Current GHG1 GHG2 Current index £/cow £3.40 £2.87 £2.84 GHG reduction per cow kg CO2e/cow -12.65 -14.65 -14.64 GHG reduction per kg product produced

g CO2e/kg product

-98.12 -118.03 -118.17

8. Conclusion

The results show that current ruminant selection objectives have favourable economic and environmental benefits. Altering selection objectives to target environmental goals only can further enhance the reduction in GHG emissions at a relatively small economic cost. The quantified economic losses for altering the focus of the selection objective could be classed as the cost to farmers of achieving additional emissions reductions above and beyond their current trajectory. The environmental weights calculated place emphasis on both production efficiency and system efficiency traits. However, there tends to be a larger emphasis on the production efficiency traits in the environmental index relative to the system efficiency traits. This may conflict with some of the other issues that livestock producers face as increasing the weighting on production traits can have an unfavourable impact on fitness traits. This may be contrary to some wider societal requirements of improving health and welfare of livestock on farms. Although some of the potential reduction in emissions may seem small it must be noted that genetic improvement is a cumulative benefit, with the annual reduction in emissions adding up year on year. Genetic improvement is a relatively cost-effective mechanism by which to achieve reduction in GHG emissions as there no continued input costs, above and beyond the establishment of the breeding and recording programme. Genetic improvement tools provide a useful and cost-effective mechanism to help livestock agriculture meet the challenges of the reducing GHG emissions.

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Acknowledgements

This work has been further supported from Defra and The Scottish Government. The authors would like to acknowledge the input to aspects of this work from Dominic Moran, Kairtsy Topp, Bob Rees, Mike Coffey and Tim Roughsedge (SAC); Huw Jones (Biosciences KTN); Adrian Williams (Cranfield University); and Peter Amer (AbacusBio).

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