The potential for reducing greenhouse gas emissions for sheep and cattle in the UK using genetic selection*

Eileen Wall¹, Cameron Ludemann1,2, Huw Jones3, Eric Audsley4, Dominic Moran5, Tim Roughsedge1 and Peter Amer²

¹ SAC Sustainable Livestock Systems, Sir Stephen Watson Building, Bush Estate, Penicuik, Midlothian, EH26 0PH, United Kingdom

² AbacusBio Ltd, 1st Floor Public Trust Building, 442 Moray Pl, P.O. Box 5585, Dunedin 9058, New Zealand

3 Bioscience KTN, Roslin BioCentre, Midlothian, EH25 9PS, United Kingdom

4 Natural Resource Management Institute, Cranfield University, Bedford, MK43 0AL, United Kingdom

5 SAC Land Economy and Environment, SAC, Kings Buildings, West Mains Road, EH9 3JG, Scotland

Date: February, 2010

Project funded by DEFRA (FGG0808)

*Defra project title: Would livestock breeding goals change if carbon and nitrogen efficiency, rather than economic efficiency, were the priority objectives? (IF0182)

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Table of Contents Table of Contents.........................................................................................................2Executive Summary.....................................................................................................41. Introduction...............................................................................................................62. Description of breeding goals in UK livestock species.............................................8

2.1. Sheep................................................................................................................82.2. Beef...................................................................................................................92.3. Dairy................................................................................................................102.4. Monogastric species........................................................................................12

3. Modelling Greenhouse Gas Emissions from Ruminant Systems...........................133.1. Review of alternative models..........................................................................13

3.1.1. CALM........................................................................................................143.1.2. Lincoln University.....................................................................................153.1.3. C-Plan.......................................................................................................163.1.4. Cranfield Life Cycle Assessment..............................................................173.1.5. Genetic GHG model.................................................................................173.1.6. Discussion of GHG emissions models.....................................................19

3.2. Description of the genetic GHG models..........................................................203.3. Sheep..............................................................................................................21

3.3.1. The flock...................................................................................................213.3.2. Environmental influences.........................................................................233.3.3. Management.............................................................................................233.3.4. Genetic GHG model sheep results...........................................................25

3.4. Beef cattle.......................................................................................................272.4.1. The herd...................................................................................................272.4.2. Environmental influences.........................................................................282.4.3. Management.............................................................................................283.4.4. Genetic GHG model beef results.............................................................30

3.5. Dairy cattle......................................................................................................313.5.1. The herd...................................................................................................313.5.2. Environmental influences.........................................................................323.5.3. Management.............................................................................................323.5.4. Genetic GHG model dairy results.............................................................33

4. Impact of altering breeding goals on biological, economic and environmental performance of ruminant production systems............................................................35

4.1. Methodology to predict expected responses to differing ruminant breeding goals.......................................................................................................................35

4.1.1. Selection index methodology...................................................................354.1.2. Discounted genetic expressions...............................................................364.1.3. Environmental weights in ruminant selection indices...............................374.1.4. Merging environmental and economic weights in ruminant selection indices................................................................................................................38

4.2. Results.............................................................................................................404.2.1. Hill sheep results......................................................................................404.2.2. Crossing (upland) sheep results...............................................................434.2.3. Terminal sheep results.............................................................................444.2.4. Maternal beef results................................................................................464.2.5. Terminal beef results................................................................................484.2.6. Dairy cattle results....................................................................................50

4.3. Discussion.......................................................................................................514.3.1. Sheep.......................................................................................................514.3.2. Beef..........................................................................................................554.3.3. Dairy.........................................................................................................56

5. Monogastrics..........................................................................................................595.1. Background.....................................................................................................59

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5.2. Methods...........................................................................................................595.2.1. Approach..................................................................................................605.2.2. Modulation................................................................................................60

5.3. Results and Discussion...................................................................................616. Market forces/incentives required to utilise breeding tools that reduce GHG emissions...................................................................................................................637. Conclusions and Recommendations......................................................................67References.................................................................................................................72Acknowledgements....................................................................................................76Appendix 1. Greenhouse gas emissions associated with selected sheep, beef and dairy feed types..........................................................................................................77Appendix 2. UK sheep model farms performance data.............................................78Appendix 3. Methane conversion factors for sheep and cattle..................................80Appendix 4. UK beef model farms performance data................................................81Appendix 5. UK dairy herd performance data............................................................82Appendix 6. Calculation of Discounted Genetic Expressions Coefficients.................83Appendix 7. Recorded trait responses.......................................................................84Appendix 8. Responses in hill sheep when RFI environmental weight set to zero....87

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Executive Summary

Current breeding goals in livestock species in the UK include traits related to production efficiency (e.g., product output, feed efficiency) and system efficiency (e.g., fertility, health, survival). However, the range of traits and associated index weights incorporated in selection objectives differs across and within species.

In this project we have developed a biological model (Genetic GHG model) that quantifies the impact of an independent change in a selected trait on overall greenhouse gas (GHG) emissions from an “average” ruminant system. This information was used to develop selection index weights that focus on the reduction of GHG emissions. Index weights were derived for traits that have routine genetic evaluations currently available, as well as newer efficiency traits not available routinely. The project has quantified the impact of different index scenarios on GHG emissions.

Selection on current ruminant industry breeding goals is reducing GHG emissions per breeding female (and per kg product) for the majority of sheep, beef and dairy systems. The impact of the current selection indices on GHG emissions are 1.43% and 0.01% reduction in kg CO2e/ewe/annum in crossing (upland) and terminal (lowland) sheep systems respectively; 0.76% and 0.38% reduction in kg CO2e/cow/annum in maternal and terminal beef systems respectively and 0.53% reduction in kg CO2e/cow/annum in dairy cattle systems. However, in hill sheep systems it was estimated that the current selection index was increasing GHG emissions by 0.45% kg CO2e/ewe/annum.

The impact of an exclusively environmental selection indices on GHG emissions are a 1.26%, 2.94% and 0.27% reduction in kg CO2e/ewe/annum in hill, crossing (upland) and terminal (lowland) sheep systems respectively; 1.1% and 0.44% reduction in kg CO2e/cow/annum in maternal and terminal beef systems respectively and a 1.01% reduction in kg CO2e/cow/annum in dairy cattle systems. In all cases, selection on an environmental index reduced the rate of overall economic response/animal/annum.

The economic breeding goals studied in this report (and available in the UK) are based on optimising economic performance within the farming system and generally are expected to result in a favourable environmental impact of the system. However, the expected economic and environmental impact of alternative selection goals in hill sheep systems highlights that economic and environmental goals may not always be aligned. The economic goal for hill sheep systems places a large weight on maternal traits. This weighting benefits the crossing sector of the UK sheep industry, who receives higher quality replacement females from the hill sector but without the environmental cost of incorporating a heavier weighting on maternal characteristics in their breeding goal. This highlights the fact that, in some cases, it may be better to consider an environmental goal across a production chain rather than within a system only, particularly when systems interact, as within the stratified nature of the UK sheep industry or the production of dairy beef.

The impact of a selection index that incorporated current economic weights and environmental weights based on the 2020 shadow price of carbon (£32.90/t CO2e) was shown to reduce GHG emissions by 0.42%, 2.32% and 0.05% reduction in kg CO2e/ewe/annum in hill, crossing (upland) and terminal (lowland) sheep systems respectively; 0.88% and 0.40% reduction in kg CO2e/cow/annum in maternal and terminal beef systems respectively and 0.47% reduction in kg

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CO2e/cow/annum in dairy cattle systems. This ranged from 19% (terminal sheep) to 89% (terminal beef) of the reductions in GHG that would be achieved if selection were solely for GHG mitigation.

Although results have been presented in terms of economic and environmental impact per breeding animal the results were also expressed per kg of product. Although the results did differ slightly the trend in the results were largely consistent.

Summary of the economic and environmental (per breeding animal and per unit of product output) the breeding goal is either the current breeding goal based on maximising profitability (economic) or maximising the reduction in greenhouse emissions (GHG) in defined sheep, beef and dairy systems*

Breeding objectives & responses per annum

Selection index weights used Units Economic GHGHill sheepCurrent index £/ewe £0.20 -£0.08GHG reduction per ewe kg CO2e/ ewe 1.50 -4.18GHG reduction per kg product produced g CO2e/ kg product 129.91 -376.52Crossing sheepCurrent index £/ewe £0.72 £0.35GHG reduction per ewe kg CO2e/ewe -6.64 -13.68GHG reduction per kg product produced g CO2e/ kg product -302.09 -620.27Terminal (lowland) sheepCurrent index £/ewe £0.62 -£0.01GHG reduction per ewe kg CO2e/ewe -0.04 -0.94GHG reduction per kg product produced g CO2e/ kg product -1.89 -41.81Suckler beefCurrent index £/cow £4.21 £2.86GHG reduction per cow Kg CO2e/cow -25.10 -36.09GHG reduction per kg product produced g CO2e/kg product -259.82 -283.45Terminal BeefCurrent index £/cow £3.40 £2.87GHG reduction per cow kg CO2e/cow -12.65 -14.65GHG reduction per kg product produced g CO2e/kg product -98.12 -118.03Dairy CowsCurrent index £/cow £7.11 £3.21GHG reduction per cow kg CO2e/cow -33.50 -64.07GHG reduction per kg product produced g CO2e/kg product -14.15 -28.79* Please note that figures are not directly comparable across livestock systems (i.e., beef expected responses are not directly comparable with dairy)

The main traits that impacted on GHG reductions in both current and environmental indices were those that related to efficiency, both in terms of production efficiency and system efficiency. However, the environmental index placed greater weighting on, and therefore predicted larger expected responses in production traits. The correlated impact of the environmental index on animal health and welfare traits (including fertility and longevity) tended to be less favourable than seen with the current index. Therefore, there is a potential trade-off in selection indices between environmental goals and animal welfare goals.

There may be a range of limitations placed on livestock systems that may preclude them achieving the maximum GHG abatement, via breeding and other mechanisms. These not only include issues relating to animal health and welfare, but also the impact of competition for feed and land resources into the future. Although production efficiency can have a favourable impact on GHG emissions,

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livestock may be limited to achieve the complete value of this efficiency improvement if feed is unavailable due to it going directly into the human food chain and/or bio fuels. Also, some higher grade agriculture may shift production from pasture ruminant based systems to crop based systems. Therefore a joint approach to breeding goals across all livestock species may be required to help match livestock to the range of future land use scenarios.

At present, the current index weights, based on the current economic scenario, would not incentivise farmers to shift selection emphasis from a profit goal to a GHG reduction goal. However, if carbon had an intrinsic value to the farming system (e.g., via a cap and trade mechanism) then selection emphasis could be redirected to a GHG reduction goal whilst maintaining overall farm “profitability”. The value of carbon that resulted in shifting the focus of the breeding goal in ruminant systems with a neutral impact on farm profits (i.e., no change in expected economic response compare to current index) fell between the price today (£26.50/t CO2e) and the 2020 price (£32.90/t CO2e).

The results of this study could be incorporated into many of the currently available ruminant genetic improvement tools to help producers reduce GHG emissions above and beyond the reduction potential of currently available indices. However, a shift from the current economic goal to an environmental goal would result in a slower rate of improvement in economic response – representing a cost to farmers to realise that further emissions reduction. Also, there would be an negative impact on fitness traits that may not be sustainable.

The results of this project illustrate that current genetic improvement tools, and new tools that focus on GHG emissions reduction, can have a beneficial economic and environmental response. Although some of the potential reduction in emissions may seem small in percentage terms it must be noted that genetic improvement is cumulative, with the annual reduction in emissions adding up year on year. Genetic improvement tools provide a useful and cost-effective mechanism for help UK livestock agriculture meet the challenges of the reducing GHG emissions.

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1. Introduction

Livestock production systems have a dual role, not only in food production, but also in the provision of public good objectives including biodiversity and landscape value. However, agriculture also generates external costs or negative public goods; specifically, diffuse pollution to air and water. Mitigating greenhouse gas (GHG) emissions from livestock is increasingly recognised as a necessary part of the UK’s overall climate change obligations. Under The UK Low Carbon Transition Plan1, the UK Government plans to cut farming and waste emissions by 6% of 2008 by 2022 as part of the more long-term targets set out in the UK Climate Change Act 2008 for reducing national emissions by 80% of 1990 levels by 2050.

Approximately three quarters of the UK land area (18.6 m ha) is classed as agricultural land (including woodlands) of which 11.7 million hectares are under grass. This grass supports a ruminant animal population of 10.3 million cattle and 33.9 million sheep. This is coupled with a total of 4.8 million pigs and 167.7 million poultry (Agriculture in the United Kingdom 20072). Overall, agriculture (and land-use) account for the 7% of the total UK greenhouse gas emissionsError: Referencesource not found. Livestock systems (there are over 220,000 livestock holdings in the UK) are an important source of GHG emissions, particularly methane (CH4) and nitrous oxide (N2O). Both ruminant and monogastric species produce N2O from manure management. Ruminant production (cattle and sheep) needs to consider both CH4 and N2O, whereas monogastric production (pigs and poultry) species are mainly concerned with N2O (and ammonia, NH3).

There are many possible technical mitigation options for livestock systems. These could be delivered through improved livestock and livestock system efficiency - converting more energy into product output, thereby reducing GHG emissions per unit product. One of the tools available to farmers is genetic selection. Genetic improvement of livestock is a particularly cost-effective technology, producing permanent and cumulative changes in performance. Mechanisms by which genetic tools could be used to reduce emissions per kg product include:

1. improving productivity and efficiency; 2. reducing wastage at the herd or flock level; and 3. reducing emissions by direct selection, if or when individual animal

emissions are measurable.

A recent study (Moran et al., 2007) has shown the very high value of animal and plant genetics research and development in helping to deliver on policy priorities, including responding to global climate change and reducing the environmental impact of farming systems. This research showed that plant and animal genetic improvement is expected to deliver public good rates of return ranging from 11 to 61% for the case studies examined - many times higher than the 3.5% recommended UK Government Treasury rate of return for public investment.

The aim of this project was to examine the role that past, current and future breeding goals in UK livestock populations can have on GHG emissions with the specific objectives of: 1. Quantify the impact of current livestock breeding goals on GHG emissions

1 The UK Low Carbon Transition Plan: National Strategy for Climate & Energy (http://www.decc.gov.uk/en/content/cms/publications/lc_trans_plan/lc_trans_plan.aspx) 2 https://statistics.defra.gov.uk/esg/publications/auk/2007/18%20Whole%20publication.pdf

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2. Estimate the effect of changing breeding goals to consider environmental impact as the main driver

3. Elucidate the market forces/incentives required to utilise breeding tools that reduce GHG emissions

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2. Description of breeding goals in UK livestock species.

2.1. Sheep

The UK sheep industry is characterised by a stratified system where different breeds of sheep are used according to the diverse nature of the landscape from hill to lowland. Genetic evaluations are undertaken via SheepBreeder (Signet/EGENEs) in a similar system as beef genetic evaluations. However, sheep genetic evaluations can be within breed, within group or even within flock. 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) and helps breeders to select animals with potential to produce progeny that will finish quickly and have good carcass weight and conformation without being overfat. This index does not include maternal, health or fertility traits since the main use of terminal sire breeds is in siring slaughter lambs.

By contrast, the hill index includes fertility and maternal traits (e.g. litter size, maternal eight week weight, mature size, longevity) along with lamb growth and carcass traits as it is targeted towards improving the financial productivity of the hill ewe, by increasing numbers of lambs and lamb carcass quality, without compromising the ability of the ewe to survive in the harsh hill environment (Conington et al., 2001, 2004, 2006a). This index is generally used in Scottish Blackface and Cheviot breeds. The Welsh Index, similarly to the hill index, was designed to help breeders select for both maternal traits and improved carcass quality, which will both influence the physical and financial performance of the flock, but is used mainly in Welsh breeds such as the Welsh Mountain. The Maternal Index aims to enhance lamb survival and pre-weaning growth by improving maternal ability and includes traits such as litter size, 8-week weight, mature size and maternal ability. Selection on the maternal index would result in females, and a ram's progeny as replacements, that will milk well, are prolific and lamb regularly. The maternal index is likely to be used in longwool crossing sire breeds such as the Bluefaced Leicester. The terminal index was updated in 2000 to include the more accurate measures of carcass composition that had become available through CT scanning (CT fat and lean weights) and then again more recently to include a measure of hind leg muscularity (CT gigot score). The hill index has recently undergone an evaluation to determine whether inclusion of CT scanning traits would improve the selection of hill sheep for carcass quality. Although inclusion of CT traits would lead to improvements in carcass quality (Conington et al., 2006b; Lambe et al.,2008) it might be that these are not sufficiently large to justify the extra costs of recording these traits because of the more complex nature of this multi-trait index compared to the terminal index.

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Table 2.1. Goal and index traits and respective index weights for the selection of indices available for the UK sheep industry.Trait group

Traits WeightTerminal Hill Lowland Welsh Longwool

Production 8 week wt (kg) -0.252 1Scan wt (kg) 0.378 11.814 0.49 0.1698Muscle depth (cm) 1.09 12.692 1.11 1.542Fat depth (cm) -1.916 -4.106 -1.56 -1.2783Mature size (kg) 0.367 0.15CT (computer tomography) lean score*CT fat score*CT muscle score*

Health Faecal egg count (count)*

Fertility Litter size born (count)

361.1 1.3 -0.1188

Maternal 8 week wt (kg)

11.415 1 0.83

* Breeding values for these traits are calculated and used by some breeds in breed/group specific indices not described in this table

2.2. Beef

There are 2 main service providers for genetic evaluations of UK beef breeds. The first is EGENES on behalf of Signet and the second is the Australian system BREEDPLAN run by Agricultural Business Research Institute (ABRI), based at the University of New England, Armidale. Central to the UK evaluation system is the BASCO database. The individual breed societies, Signet and individual members all enter pedigree, performance and fixed effects (e.g. birth dates, sex etc) data into the BASCO database. To be included in the genetic evaluations, breeders register with Signet and Signet then coordinates performance recording for the herd. Some of the performance measures are measured on farm by the breeder (e.g. live weight) while others are measured by trained technicians (e.g. ultrasound scans). Genetic evaluations are usually undertaken between one and three times a year, depending on the needs of the individual breeds. At agreed time points, EGENES extracts information from BASCO to undertake genetic evaluations. The genetic evaluations produce BLUP EBVs as well as selection indices. These EBVs are then loaded onto the BASCO database for stakeholders to access.

As with dairy indices 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). Overall, there are 3 sub indices available in beef, namely the Beef Value and Calving Value sub indices which are combined to form the Terminal Sire index and the Maternal Value Index (Table 2.2). All 3 sub indices are combined to form the Maternal Production index which has both carcase and maternal traits in the breeding goal. Economic value estimation for the Beef Value traits are based on combined biological prediction equations which simulate growth and carcase characteristics for a group of animals over time, with optimal economic rotation theory. More complex models that account for the threshold nature of calving ease data have also been developed to account for the costs of each type of assistance given at calving ranging from no assistance to caesarean section

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(Roughsedge et al., 2005). The models for current economic values for fertility and longevity traits are further described by Roughsedge et al. (2005).

Table 2.2. Goal traits, economic values (EV) and index traits for the selection of indices available for the UK beef industry.Index Goal Trait Economic Value Index TraitBeef Value

Carcass Weight £1.2/kg Birth, 200 & 400 day weight

Carcass Conformation Score £7/unit Muscling ScoreUltrasonic Muscle Depth

Carcase Fat Score -£6/unit Ultrasonic Fat Depth

Calving Value

Gestation Length £1/day Gestation Length

Direct Calving Ease -£2.88/% increase Calving Difficulty ScoreBirth Weight

Maternal Value

Calving Interval (days) -£0.83/day Calving IntervalAge First Calving (%)* -£48.11/% Age First

CalvingLifespan (disposal age in years) £6.63/year LifespanMaternal Weaning Weight £0.73/kg 200 Day WeightMaternal Calving Ease (%) -£2.19/% increase Calving

Difficulty Score* Please refer to Table 3.7 for further detailed descriptions of traits.

2.3. Dairy

Improving the quality of breeding stock is one of the most cost effective ways of improving the long-term profitability of livestock enterprises. For this to happen effectively, producers need information to help them identify the best breeding stock for those traits that affect profitability. In the case of dairy cattle, farmers have to undertake an optimisation to maximise production whilst minimising costs of production. In modern dairy cows, reduced profitability is increasingly becoming associated with health and fertility costs of maintaining the dairy herd. Getting reliable information on traits of economic importance in dairy cows requires three key activities: Recording performance in breeders’ herds for traits affecting profit (‘milk

recording’). This requires an investment of time and money by breeders, but it increases returns because of the added value recording brings to commercial milk producers in day to day management of nutrition and health.

Using these performance records together with pedigrees, to predict which animals are likely to produce the best progeny (‘genetic evaluation’). This is technically demanding, but there is good agreement internationally on the methods used, and efficient software and powerful computers available to do it in most countries. Above the value of milk recording for management purposes, cow genetic merit also has value – most milk recording breeders in the UK get a premium when selling surplus stock. When combined with functional trait assessment the premium can be even bigger.

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Providing results to breeders and their commercial producer clients in a ‘user friendly’ way. Dairy genetic evaluations produce Predicted Transmitting Abilities (PTAs) that give a guide to the animal’s genetic merit for individual traits. Indexes which are a combination of traits are often calculated from PTAs to identify animals with the best all round genetic merit for production and health characteristics (£PLI). While it is easy to transmit results to breeders and into sale catalogues etc., additional technical and advisory backup is vital if the results are to be widely understood and used appropriately.

Milk production traits currently analysed are milk, fat, protein, fat percent, protein percent and persistency. The analyses of these traits use the individual test day records for each of the first five lactations. PTAs are then adjusted to the current base, which is for cows born in 2005.

Current health and fertility traits evaluated are somatic cell count (SCC) and udder/mammary composite to predict mastitis; locomotion to predict lameness; calving interval (CI) and non-return rate (NR) to predict fertility and lifespan (LS) to predict longevity. Direct and maternal calving ease proofs have been available since January, 2010 as a result of a Defra LINK project, Expanding Indices.

In the UK the national dairy breeding goal is based on the economic breeding index, Profitable Lifetime Index (£PLI). The goal of £PLI is to improve overall system profitability by selecting on the goal traits of production, lifespan, health (mastitis and lameness) and fertility as both production and fitness traits can impact of overall system profitability. The goal and index traits and the economic values used in the current £PLI index are given in Table 2.1. A broader index, such as £PLI, was shown by Stott et al. (2005) to have clear financial benefits for the farming industry together with improvements in animal health and welfare of benefit to society as a whole. The current overall weighting on production traits in £PLI is ~ 50%, with 50% weighting on fitness traits. The economic values for £PLI were calculated in 2003 by the current contractors.

Table 2.3. Goal traits, economic values (EV) and index traits for the UK dairy selection index, £PLI released in August 2007 (source: DairyCo breeding+) Trait group Goal trait EV Index traitProduction Milk yield (1kg ↑) -0.027 305d milk yield (kg)

Milk fat yield (1kg ↑) 0.80 305d milk fat yield (kg)Milk protein yield (1kg ↑) 1.71 305d milk protein yield (kg)

Longevity Lifespan (+1 lactation) 25.46 Lifespan (no. of lacns survived)Health Lameness (1% ↓ in incidence) 0.91 Locomotion score (linear scale)

Feet & legs score (linear scale)Mastitis (1% ↓ in incidence) 0.96 Somatic cell count (count)

Udder score (linear scale)Fertility Calving interval (+1 day) -0.35 Calving interval (lacn 1-2, days)

Conception (1% ↑ in conception rate) 2.16 Non-return rate 56 days (0/1)Direct calving ease n/a Calving score (1-4)Maternal calving ease n/a Calving score (1-4)

With the exception of milk (which is given in £/kg/cow), the above are expressed in £/cow, annualised to 365 days.

The current £PLI economic evaluations (revised in 2007, Stott et al., 2007) are based on the outputs from a bio-economic whole herd model described by Santarossa et al. (2004) which further developed the models of Brotherstone et al. (2003) and Stott et al. (2005). The approach takes a whole herd, rather than an individual milking cow, perspective which makes it easier to incorporate traits representing ‘fertility’ that affect herd management as well as individual cow performance. The method of

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Santarossa et al. (2004) is also based on concepts of natural resource economics and so addresses issues of sustainability. Economic values of traits in the current £PLI index are produced for a ‘typical’ commercial dairy herd with biological and herd parameters from a ‘typical’ herd were provided coming from literature, reports etc. and validated by industry via DairyCo breeding+.

2.4. Monogastric species

For both pigs and poultry much of the development of breeding indices tends to be done within commercial companies. Consequently, information on the exact weights that are used in different types of indices is rarely published. The weights used may also vary between breeds or even selection lines, particularly for pigs where the breeding of separate maternal and terminal sire lines is common. Although details of specific weight are not freely available, more is known however about the kind of traits on which selection is generally done (Genesis-Faraday, 2008).

PigsThe main selection emphasis has historically being done on measures of growth rate, back fat thickness, feed conversion ratio, and also for maternal lines would also include measures related to litter size, survival (e.g. number still born, % litter mortality, number born alive) and a sows ability to re-breed (e.g. number of litter per sow per year) (Merks et al., 2000).

Broilers Selection emphasis in broilers has tended to be on measures of daily gain, food conversion ratio, killing out proportion and reducing mortality. Selection for improved health and welfare is also commonly done, such as reduced susceptibility to Ascities and lameness (McKay et al., 2000).

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3. Modelling Greenhouse Gas Emissions from Ruminant Systems

3.1. Review of alternative models

Models to calculate greenhouse gases are vital in a world where people are increasingly concerned about climate change. Without tools to calculate the GHG being produced it would be impossible to quantify if a certain farm or industry has reduced its global warming burden. Without GHG models there would be no way to compare the best mitigation strategies for complex systems such as farms with ruminants. Comparisons using models could help rank the relative profitability of mitigation options or in the case of models developed for the purposes of this project (presented later) provide an indication of the potential role of genetic improvement in altering ruminant GHG emissions.

There are many models which aim to calculate greenhouse gases for agriculture. These include simple animal emissions factors (e.g., Tier I techniques in IPCC guidelines), national inventory techniques (e.g., Choudrie et al., 2008), carbon footprinting tools (e.g., CALM and C-Plan, discussed later) and lifecycle analysis (e.g., Williams et al., 2006, IS0205; PAS 2050 guidelines). However, most models differ in the way they calculate the emissions; what output data it provides and their level of detail and accuracy. This will depend on their purpose, whether it be to provide a simple tool for farmers to make a broad assessment of their carbon footprint or to allow for more detailed and accurate analysis.

The study of Jones et al. (2008), examined the impact of historic genetic selection on GHG emissions from livestock production. Due to the assumptions and nature of the LCA model used, only a selection of the traits could be incorporated and the impact of other traits, such as health traits on direct emissions traits could not be modelled. LCA is one tool that can provide a holistic assessment of the environmental impact of farming systems.

The focus of this project is to examine the environmental impact of different breeding goals, past, current and future. It should be noted that many breeding goals, and their associated economic values, have been developed by examining how changes (e.g., genetic improvement) in individual animal traits impact on the performance, economic or otherwise, of the farming system. To model the role of genetic improvement (past and future) it is necessary to estimate the changes in the GHG emissions of the system after a genetic change in a range of biological traits at the individual animal level. The model developed in this project (Genetic GHG model), draws from other relevant models of GHG emissions but focuses on modelling the impact of changing the underlying performance of animals within a ruminant system.

Five models, including four from the UK (CALM3, C-Plan4, Cranfield LCA and the model here in) and one from NZ (Lincoln University), are described with emphasis on their functionality. Output from the models will be compared to preliminary results from the upland sheep GHG model developed for this project (described, in detail, later). To provide a relevant comparison of GHG figures between the models, only the emissions associated directly with ruminants were put into the models (i.e. fertiliser, capital, land use change and fuel emissions were excluded, so that it would be comparable with the model developed as part of this project).

3 http://www.calm.cla.org.uk/4 http://www.cplan.org.uk/calculator.asp

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Table 3.1 provides a summary of the estimates of GHG emissions from the 5 models under review and shows the estimated GHG emissions associated with sheep production from each of the models. The different reporting mechanisms of each of the models make it difficult to compare results directly. Therefore, manual calculations were made to allow for comparison of results across models.

Outputs ranged from 7kg carbon dioxide equivalents (CO2e)/kg lamb carcass weight using the C-Plan model to 28kg CO2e using the CALM model. C-Plan only had two classes of stock including ‘annual average number of sheep’ and ‘average number of lambs’. There was reasonable agreement between the Cranfield LCA model and the Genetic GHG model. However, the Cranfield model took into account all the sources of GHG in its calculation, including those in fixed assets, where as the Genetic GHG model focussed on emissions related to characteristics of animal performance and excluded asset GHG estimates. It is interesting to note that the basic Lincoln University model came up with an emissions per kg lamb figure close to Genetic GHG model. However, the Lincoln model is limited in terms of measuring changes in GHG related to trait change.

The CALM produced relatively high emission results. This could be attributed to the fact that hoggets had to be put into one of three categories; with the closest category being ‘the ‘other sheep over 1 year’ category. This category is likely to have overestimated the hoggets emissions.

Table 3.1. Comparison of greenhouse gas emission estimate for sheep across a range of models.

ModelsGenetic GHG model1

CALM Lincoln University

Cplan Cranfield LCA

Emission unitsTotal (kg CO2e) 46503 68000 59000 15620 n/aKg CO2e/kg lamb Cwt 22 28 n/a 7Kg CO2e/kg lamb+ cull ewe Cwt

16 21 17 5 17

Variability as a % of Genetic GHG model

~+127% to +33%

1 Model developed in this project to estimate the impact of changing individual biological traits one by one to model the impact of genetic improvement on GHG emissions.

3.1.1. CALM

CALM stands for ‘Carbon Accounting for Land Managers’ and is a free model developed in partnership between the Country Land and Business Association (CLA) and Savills.

Calculation methodsThis model aims to estimate the balance between annual emissions and carbon sequestration and is limited to Tier 1 IPCC methodology.

Output dataCALM produced a comprehensive table of the carbon balance for the ‘model farm’. It categorised 10 sources of carbon emissions including, ‘dairy cows’, ‘cattle and sheep’ in one category, ‘other livestock’ in another category as well as ‘lime’ , ‘energy’ and ‘land use change’ categorised separately. It also had three forms of carbon sequestration to complete the carbon balance. Land use change, farm woodland (for general farm use) and farm woodland (for commercial forestry) made

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up the sources of carbon sequestration. Results were displayed in tonnes of carbon dioxide, methane, nitrous oxide as well as in carbon equivalents.

Detail and accuracyAlthough the model differentiated the sheep class of stock into three categories including ‘breeding sheep’ (ewes and rams); ‘other sheep over 1 year’; and ‘lambs under 1 year’, there were still limitations. Firstly, this level of differentiation does not allow rams and ewes to be differentiated. This could lead to significant differences especially since ewes can have elevated energy requirements (thus methane emissions) due to pregnancy and lactation demands. While the ‘other sheep over 1 year’ allows further differentiation of the older sheep, it is not clear what kind of sheep it is. There could be significant differences between animal classes, for example wethers that are 4 years old and growing sheep that are just over 1 year of age.

However, in spite of its limitations this model does provide a relatively easy way of determining a farm system’s overall carbon balance. The carbon sequestration section of the balance would give farmers something to relate to when comparing their emissions (i.e. how many hectares of soil or trees are needed to offset the emissions. However, soil sequestration is a complex topic and the method applied in the CALM model which involves just inputting the area of land that turns from grass to arable, arable to grass, grass to woodland etc. is simplistic. Soil carbon tests would be a more accurate way of getting soil sequestration levels. However, soil carbon sequestration is a slow process, with Post and Kwon (2000) noting that the maximum rate of carbon accumulating during early aggrading stages of perennial vegetation were about 100g/m² pa (1 tonne/ha/pa). This can make it difficult to measure changes in the short term. The sequestration side of the carbon balance is therefore going to be exposed to significant error.

Overall, this model provides a good method to give an overall carbon balance for farmers interest. However, the detail of input data is such that it would not be capable of accurately depicting subtle changes in stock performance for the purposes of calculating the potential for genetics to reduce GHG.

3.1.2. Lincoln University

The Agribusiness and Economics research Unit (AERU) and AgriLINK developed a simple carbon foot printing model designed for farmers to use it for benchmarking, setting targets and monitoring their carbon footprint. The model was put up for free on the Lincoln University (New Zealand) website.

Calculation methodsCalculation methods were limited to IPCC (2006) Tier 1 methodology, however a full account of the methodology for this model was unavailable.

Output data The Lincoln carbon calculator divided the carbon output into four headings including, ‘energy’, ‘fertiliser/feed’, ‘methane’ and ‘nitrous oxide’. Results were based in units of total CO2e, and CO2e/ha. There was also the option for it to calculate the carbon emissions into CO2e/kg of meat or wool product.

Detail and accuracyIt was intended for use by farmers to benchmark, set targets and monitor carbon footprints. Unfortunately, the simplicity of details required to put into the model meant that it would be difficult to accurately estimate management change impacts on total farm GHG. For instance, the input of sheep numbers was limited to one category.

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Therefore the model would not be able to calculate a GHG change due to many changes in stock mix. For instance reducing the number of rams while increasing the number of ewes or having fewer but more fertile ewes may in some cases did not cause a change to total farm GHG as it should when using the Lincoln model. Not differentiating the stock into more classes means significant error would arise based on the model not accurately reflecting the GHG differences between stock weights, gender, and performance etc. Its simplicity therefore precludes the model from being able to estimate subtle changes to livestock genetics.

The benefit of the model is that it does provide an easy way for farmers to get some idea of their farm emissions without the need for a consultant. This will, in turn, make them more aware of GHG issues. The downside to the model comes if the farmers use the model to try and estimate GHG changes without realising the limitations to its accuracy. This indeed could prove detrimental as it could lead to poor decision making surrounding GHG on the farm.

3.1.3. C-Plan

C-Plan was initiated by a farming couple (Drew and Jan Coulter) from Central Scotland who were interested in what their carbon emissions were, and to have facts available to challenge government/ EU agencies if need be.

Calculation methodsIPCC Tier 1 methodology was implemented in the C-Plan model. However, an updated version (V2) is available at a cost of £29.99 per calculation. The advantage of the V2 model is that Tier 2 methodology is used allowing the farmer to look into ‘what if’ scenarios. There were some Tier 2 tendencies in the first version of the C-Plan model. This included the ‘Land use change’ option of selecting between England, Wales, Scotland or Northern Ireland. It also had the option to select between mineral or peaty soils. This may raise the accuracy of this model’s soil sequestration estimates relative to CALM, but overall it would still be a crude tool for calculating soil carbon balances. Nonetheless, for this analysis the models were only used for their livestock emission estimates (land use changes were excluded).

Output data The summary of emissions was kept simple in the C-Plan model. Results were estimated in carbon equivalents and not broken down into the proportion of emissions that were carbon dioxide, methane or nitrous oxide. Carbon equivalent emissions were broken down into 6 areas of emissions namely: ‘energy/fuel’; ‘livestock’; ‘fertiliser’; ‘crops’; ‘forestry’; and ‘land use change’. The latter two categories could also indicate the levels of carbon sequestration if there was a net planting of forestry or a net increase in soil carbon through land use change.

Detail and accuracyUnlike the Lincoln University model, the C-Plan model divided its classes of stock into two, including ‘sheep’ and ‘lambs’ to improve its accuracy. However, two classes of stock are inadequate for truly accurate estimations of emissions, especially when looking into subtle changes to certain classes such as mature weight of ewes. Also, juvenile sheep such as growing hoggets will not be accounted for correctly. Their GHG maybe underestimated if hoggets were put into the lamb section, or overestimated if put into the sheep section. There is no indication of how the ‘sheep’ was defined either (i.e., in terms of its weight, gender or age) making it difficult for the user to try and add the hoggets as ‘sheep’ equivalents. For example, perhaps a 50 kg hogget could be put in as 0.8 of a 60 kg ‘sheep’ if it was known that the sheep was defined as being 60kg live weight.

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3.1.4. Cranfield Life Cycle Assessment

Researchers at Cranfield University developed this Life cycle assessment model in 2006 to provide a systematic and holistic method to assess resource use and environmental burdens from different methods of Agricultural and Horticultural commodities. The model was used to compare the production systems of 10 commodities including bread wheat, oilseed rape, potatoes, tomatoes, pig meat, poultry, beef, sheep meat, milk (cattle) and eggs (chickens). The primary aim was to compare across commodities as opposed to comparing changes within a production system.

Calculation methodsThe principles of life cycle assessment were used to develop this model, with the farm gate being used as the system boundary. This included the land to a depth of 0.3 metres. Inputs to the system were traced back to their original source such as mined ore, crude oil and coal.

Output data A wide range of detailed results were calculated by the LCA including those related and not related to GHG. The results section calculated the emissions in carbon equivalents per 1000kg carcass weight. The emissions were allocated to the different products from the livestock enterprise. For sheep it allocated emissions between mutton, lamb and wool using the economic allocation theory. Carbon emissions were allocated to each kilogram of product depending on its relative economic value. Wool, for example, for most current UK sheep systems would have less emissions allocated to it compared to lamb meat due to its relatively low value. Non GHG items were also reported in the results including values for eutrophication and acidification.

Detail and accuracyBoundaries to the Cranfield LCA model were set to the farm gate making it more relevant to the Genetic GHG model as opposed to many LCA models which model changes from cradle to grave (Williams et al, 2009). However, the LCA models were not based on a ‘typical farm’. Rather it was based on calculating average emissions over the whole industry. Assumptions included the proportion of sheep that come from hills, uplands and lowlands and their contribution to the amount of lamb carcass produced. Beef meat for example was based on the assumption that the cattle come from a range of land classes. One point seven percent came from grade 2 land, 33.9% came from grade 3a land, 35.6% came from grade 3b land and 28.8% came from grade 4 land.

The purpose of the Genetic GHG model was to estimate GHG changes within a specific production system (i.e. hill sheep). Whereas the LCA models were based around calculating the average emissions for a commodity based on the whole industry in the UK. Therefore the scope of the GHG emissions from the LCA was too general and could not focus in on a specific production system as was needed to model genetic improvement within the context of UK ruminant production systems.

3.1.5. Genetic GHG model

This model was developed as part of this project to estimate subtle GHG changes in sheep, beef and dairy cattle when traits were altered. The underlying purpose of the models was to help investigate the potential to use genetic selection as a tool to reduce ruminant GHG.

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Calculation methodsIPCC (2006) formulae were used as the basis for developing the greenhouse gas models. UK specific energy coefficients and other parameters where available were put into the model moving it from Tier I (the lowest) methodology toward the higher Tier II and III. Tier I methodology generally only estimates changes in GHG brought on by changes in livestock numbers.

Only emissions that related directly to the stock were included. Emissions from capital, fuel, power etc. were assumed to remain constant regardless of changes in biological traits of the animals. Thus they were left out of the comparisons. Methane and nitrous oxide were the only emissions modelled.

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 (i.e. hill sheep). Each system was set to 100 (125 for dairy) breeding females.

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 such as lamb growth and ewe/cow fertility were altered separately and the total GHG for the farm was recorded to compare to the ‘base scenario’.

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.

Output data Numerous values were available to show the breakdown of emissions and how they changed when traits were altered. This included emissions, for example, per kg of lamb and/or cull ewe carcass weight. Being able to see GHG changes both in absolute terms and per kilogram of product is important because there can be antagonism between the two when traits are altered. For instance, increasing female fertility may increase total emissions over the whole system, but reduce emissions per kilogram of product.

Detail and accuracyThe Genetic GHG model was developed with the purpose to estimate the GHG changes to a typical farm system when genetic traits in sheep, beef and dairy cattle were changed. The model does not estimate all emissions from a farm as the other models in this review did. Instead it explores in more detail the methane and nitrous oxide emissions associated with sheep and beef cattle. This quantification of beef and sheep enterprise emissions was done to a higher level of accuracy in comparison to the other models owing to its methodology. Differentiating the stock into more classes and enabling each class of stock to have their key performance

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parameters altered enabled greater accuracy in estimating the GHG implications for genetically altering traits.

There is sufficient detail to estimate most general traits in the Genetic GHG model. However, the models may struggle with some traits which are difficult to measure in terms of their phenotype such as disease tolerance. Although, some of the disease symptoms like loss in live weight gains, longevity and fertility could in theory be changed in the model to indirectly estimate the effect of the disease trait. This assumes accurately quantified relationships are available between the disease trait and the detrimental symptoms which in reality can be difficult to obtain.

3.1.6. Discussion of GHG emissions models

The Genetic GHG models developed fro the purposes of this project were designed not only to consider the complete range of GHG emissions associated with changes in animal performance but also the effects of resource constraints (i.e. total grass availability in the system) on the GHG budget of the system. As a consequence the model was designed specifically for the purpose of estimating GHG effects of altering sheep and beef cattle traits over the whole of the UK. The model framework would allow further exploration of not only looking at genetic improvement as a mitigation tool but also other tools such as dietary modifications. It could also be used for more specific scenarios so that better allocation of resources for combating climate change in the agricultural sector can be made.

Moran et al. (2008) stated that to achieve emissions reductions in an economically efficient manner some attempt had to be made to abate the cheapest units of GHG first. These authors said this involved attempting to equalise abatement costs not only across sectors but within sectors. Further specificity in the GHG models could be developed so that comparisons could be made between the devolved administrations of the UK. For instance, this could allow a comparison of how cost effective genetic methods of improvement are between hill sheep farms in Scotland versus hill sheep farms in England. Thus it would enable a more targeted allocation of resources to the area which proves most cost effective. The area with lower cost effectiveness could have resources allocated to it that target other mitigation strategies. If for example improvements in pasture quality were more cost effective than genetics in the Scottish hill country (relative to England’s hill country) the resources could be divided accordingly. This would mean the cheapest GHG would be abated first in both areas leading to lower costs of GHG mitigation over all the UK.

Limitations to the idea of improved specificity to certain areas will be the availability of basic research to support the models. A good example of this is for the management of beef cattle manure. The proportion of manure managed by each method (i.e. slurry or dry lot was extrapolated from a survey of Wales and England beef farms as information on Scotland and Northern Ireland were lacking). Scotland and Northern Ireland could differ significantly in how their manure is managed. More specific data on manure management methods as well as a range of other assumptions would be necessary to make the more regionalised GHG models accurate in their emissions estimates.

The GHG models could be made more accurate in terms of livestock mature weights and live weight gains. The models assumed mature stock maintained a fixed live weight throughout the year. In reality fluctuations in weight occur. For example the annual fluctuation in beef cattle weight can be comfortably managed so that they lose up to 10% of their post weaning live weight (Morris, 2007). There is a net energy cost associated with this loss of body weight assuming it is gained back at a later stage in

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the year. As mentioned by Morris and Smeaton (2009) it is approximately 25 MJME net loss per kilogram of body weight lost. Fluctuations in live weight represent a net increase in energy intake in order for cattle to get back to the average live weight. This represents associated GHG emissions that are not accounted for in the current models.

There is also simplicity to how live weight gains of offspring were estimated. Lambs and calves were assumed to grow at predetermined live weight gains to attain set carcass weights. What the model did not fully take into account was the impact feed quality can have on the performance of growing animals. The models were able to estimate changes in GHG due to pasture digestibility. However, this was based on the impact feed digestibility had on the ratio of net energy available for growth in a diet to digestible energy consumed (R.E.G). It did not account for additional benefits of improved pasture digestibility on offspring live weight gains thus number of days to slaughter. A more accurate growth model linking pasture quality to offspring live weigh gains could enable a better estimate of the impact a non genetic influence such as feed quality (expressed in terms of feed digestibility) could have on improving GHG in a ruminant system.

Once again the success by which this growth model could obtain these more accurate GHG estimates would be determined by availability of lambs and calf growth/GHG emission trial data. This helps to provide relationships between the type and quality of feed given to offspring and their subsequent performance and GHG emissions. Much of this work in sheep has been in NZ but less so in the UK (Waghorn and Clark 2005).

The Genetic GHG models currently define 3 “average” and representative sheep systems including purebreds (i.e. Scottish blackface) mated in the Hill; crossing of the purebreds with other breeds such as Blue faced Leicester in the uplands and crossing cross bred ewes with terminal sires (such as Suffolk or Texel) in the lowlands. There is a trend in the sheep industry now for some farmers, especially in the lowlands to produce replacements off crossbred mothers that were mated with terminal sires. The Genetic GHG model only dealt with terminal traits in the lowland scenario. With the rising trend in farmers breeding replacements from crossbreds mated with terminal rams there should be more account of maternal traits in the lowland flocks. A separate index could be set up for the lowlands.

The Genetic GHG models have been developed in such a way so that individual farm scenario data could be inputted rather than the average farm. Localised farm data could be put into the models to provide the farmer with feedback on how much progress could be made on his or her farm using their current or planned breeding program. They could then weigh up for themselves whether it is worthwhile to aim for GHG mitigation traits in their flock or herd.

3.2. Description of the genetic GHG models

Three UK sheep, two UK beef and one dairy production systems were modelled to calculate changes in greenhouse gas emissions when selected production traits were altered. Although, six system models may seem limited in terms of reflecting the variability that exists between sheep, beef and dairy farms in the UK, these systems were chosen in line with models developed for breeding goals in the UK. Therefore the objective was to build models similar to those developed to calculate the original economic selection indices for each production system.

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IPCC (2006) formulae were used as the basis for developing the greenhouse gas models. UK specific data such as energy coefficients and other parameters were put into the model where available.

Methane and nitrous oxide gases relating to the livestock were the emissions included in the models. Consequently, emissions associated with capital such as housing were not included in this investigation as they were assumed to be insignificant. In addition, the net annual carbon dioxide emissions from livestock were assumed to be zero in accordance with IPCC (2006).

The numbers of UK organic sheep and beef farms have increased significantly recently, but still contribute a small proportion of total UK production. DARDNI (2006) for example stated that only 3.4% of UK agricultural land in 2005 was farmed organically. Therefore, organic sheep and beef systems were considered beyond the scope of this research. The production and performance data shown in Appendix 2 and 3 thus represent conventional UK sheep and beef farm systems.

The model was set to a limited number of breeding ewes or cows and was not based on a set quantity of energy. However, when production traits were altered it had an effect on livestock energy requirements, thus how much pasture/supplement they consumed. Carbon emissions were attached to every megajoule of pasture or supplement consumed above or below that consumed by the base model for each flock/herd type. This aimed to account for the added greenhouse gas cost or benefit when more or less pasture was consumed on the model farm relative to the base scenario. This followed an approach similar to that implemented by Jones et al (2004). Appendix 1 depicts the carbon costs attached to feed used in the model in order to account for changes in livestock energy requirements when traits changed.

The Genetic GHG model for each of the sheep, beef and dairy scenarios was utilised to estimate the impact of changing selected traits by a unit while holding all others constant. This is a similar method by which economic values are calculated. The change in the overall GHG emissions from changing the trait is then the environmental impact of improving the selected trait and is akin to the environmental value by which to weight the trait in an index. These results were in CO2 equivalents (CO2e) and were expressed in terms of per breeding animal (per breeding ewe/cow for beef and sheep, per dairy cow for dairy) and per kg of product output. These weights can all be used to estimate the expected overall environmental impact of selection on a range of indices. It should be noted that it is not possible to calculate an environmental response for all traits that may be currently considered in breeding goals. This is due to the limitations of the models/equations as well as the limited information on the relationships between traits (e.g., individual health traits) and overall GHG emissions above and beyond animal numbers.

3.3. Sheep

3.3.1. The flock

It is generally accepted that sheep breeding and production in the UK follows a stratified crossbreeding structure and can be divided into three basic areas of; ‘Hill production’, ‘Upland production’ and ‘Lowland production’ (Pollott and Stone, 2004) . Similarly this research divided the UK sheep industry into the three aforementioned areas. The biological and herd parameters used in the greenhouse gas models were similar to those used for the selection indices described by Conington et al. (2004), Haresign et al. (2007), and Jones et al. (2004) respectively.

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It was assumed the ‘Hill’ flock conformed to the traditional UK style of hill farming involving the use of purebred Scottish blackface ewes which produced purebred blackface lambs. ‘Upland’ production was assumed to involve the crossing of the Scottish Blackface ewe with Bluefaced Leicester rams to produce North Country Mules. While its population is decreasing, the North Country Mule is still the most common crossbred ewe/breed comprising 12.6% of the national breeding population (Pollott and Stone, 2004). The lowland model was focussed on cross bred (Mule) ewes mated to terminal sires to produce three way cross lambs for the prime market. The decline in Mule ewes has generally been offset by a rise in the terminal and hill breeds of ewe (Pollott and Stone, 2004).

Parameters and performanceWhere possible, parameters from the peer reviewed papers that described the development of the respective selection index were used. Any absences of required parameters in the respective papers were filled in by using one of the other selection index papers.

Assumptions and parameters were commented on by specialist consultants from the Scottish Agricultural College (SAC) to ensure they were representative of UK averages today. When defining each parameter there was a trade-off. The trade-off was between ensuring each figure was deemed realistic by the consultants and maintaining consistency with the original index models the GHG models tried to align to. An example of this trade off was for lamb weaning ages. While the consultants agreed that weaning ages for lowland lambs should be approximately 112 days, the original figure by Jones et al (2004) was 81 days. In this case the final figures erred toward the more realistic consultants recommendations. Therefore weaning ages and lamb live weight gains recommended by expert sheep consultants were used. But in general, figures that aligned to the original index models were used to build the GHG models.

When growth traits were altered in the models the associated increase in mature weight was not taken into account. Instead traits were treated independently to each other. In addition, the mature weight of sheep remained at a fixed level. No account was taken of possible fluctuations in the live weight of mature animals throughout the year.

Appendix 2 lists the main performance measures for the 3 sheep flocks. Across all three sheep models the number of ewes was limited to 100 for simplification following the previous methodology for estimating economic weights for the use in breeding goals (e.g., Conington et al., 2004).

Sheep in the three areas were assumed to have average genetic qualities, therefore average performance measures for the system were used. This included ewe prolificacy for the hill, upland and lowland of 111%, 175% and 193%, and offspring survival (from birth to weaning) of 89%, 85% and 83% respectively. The average lamb survival reduced from hill to upland and lowland systems because of the relationship between ewe prolificacy and proportion of a ewe’s litter as singles, twins and triplets. A higher proportion of a ewe’s litter becomes multiple lambs at higher prolificacy. These lambs have significantly lower survival rates as is depicted in Appendix 2, hence the lower overall survival rates.

Ewe death rates were 4.5%, 3% and 3% per annum respectively for hill, upland and lowland sheep systems, while a 7% barren ewe rate was used for all 3 systems.

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The proportions of lambs in the ewe litter as singles, twins and triplets were calculated using formulae described by Conington et al. (2004) and depicted in Equation 3.1. Equation 3.1 was used in combination with average single, twin and triplet birth to weaning survival rates (Appendix 2) to more accurately estimate the number of lambs available for slaughter.

Equation 3.1. Proportion of ewe litter as each birth ranking.Equation for singles = 3- 2.5 µ + 0.5 µ ² + 0.5 δ²Equation for twins = 4µ-3- µ²- δ²Equation for triplets = 1- proportion of singles- proportion of twinsWhere µ is the mean ewe litter size per ewe lambing (ewe prolificacy) and δ is the variance of litter size (assumed to be 0.36)

Methane conversion factors can make a significant impact on the output of the models owing to the significant proportion of total emissions methane contributes to a ruminant farm system. As shown in Appendix 3 the conversion factors varied between 3.5 to 6.5% (gross energy as methane) according to class of stock and whether the animal was housed indoors.

3.3.2. Environmental influences

TemperatureTemperature can affect the maintenance energy requirements of ruminants. Areas with average winter temperatures below 20º C require an increase in the maintenance energy coefficient to reflect the added energy demand required for maintaining body temperature (IPCC, 2006).

It was assumed the UK has an average winter temperature of 3.7ºC (Met office 2008). Accordingly, all three models had 3.7ºC set as their average winter temperature. In reality there would be variation in average winter temperatures experienced by sheep in the hills, upland and lowland which does set a limitation for comparison between the models. The consequence of using an average temperature is that energy requirements could be understated in the cooler hill country situations (i.e. for the hill sheep), and the energy requirements of the lowland sheep may be overstated. However, considering the lowland sheep will be housed indoors for 6 weeks before parturition they will be less exposed to the winter temperature effect, so the overall effect would be reduced. It should be noted that the impact of this housing period on overall system emissions was not significant from a system where the housing period was absent or shorter as it had only a small influence on the energy requirements for maintenance.

The ‘finishing lamb’ class of stock would not be exposed to the cold winter environment as they were assumed to be slaughtered before that time. This meant that their respective coefficient for calculating net energy for maintenance was not altered for temperature and were set to values (0.236 for ewe lambs and increased by 15% for entire lambs) depicted in IPCC (2006). All other classes of sheep were assumed to have an elevated maintenance energy coefficient to reflect the winter temperatures of the UK except for the lowland ewes which were housed indoors 6 weeks prior to lambing.

3.3.3. Management

Pasture management and qualityWhile pasture quality can be influenced by the environment (i.e. drought, flooding etc.) appropriate pasture grazing management can actively enhance pasture quality

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(Litherland and Lambert, 2007). Management can influence feed quality to a greater extent if stock are kept indoors. The manager can then control exactly what feed the stock consume, whether or not this is highly digestible concentrates or lower digestibility straw. The number of days which sheep in the three systems were kept indoors is shown in Appendix 2. Only the lowland sheep were housed indoors for the 6 weeks leading up to lambing. The hill, upland and lowland sheep systems were assumed to have annual pasture production figures of 4,515kg, 11,500kg and 14,000kg DM/ha pa respectively.

Feed digestibility (a measure of feed quality) is an influential factor in determining the productivity and efficiency of farming ruminants (Russel 1971). It can also have a significant bearing on methane emissions (IPCC 2006). For example a 10% error in estimating feed digestibility can cause a 12-20% error in methane emissions (IPCC, 2006).

It was discovered that the IPCC (2006) were typically conservative in their estimates of feed digestibility. Russel (1971) for example measured monthly digestibility of hill pastures when set stocked with sheep. Pastures were on average 60-65% of organic matter (Table 3.1). This compared to the ‘low quality forage’ figure set by the IPCC (2006) of 45-55%. A lower prevalence of C4 plants (e.g., maize, vs. C3 plants such as wheat) in the UK compared to other countries and the relatively well developed grazing management systems could account for this difference.

The digestibility of feed for sheep and cattle on concentrates and on pasture were therefore set in the models as the upper limits of the IPCC (2006) recommendations. Concentrate feed, flatland and hill pasture digestibility for sheep and cattle were set to 85%, 75% and 65% respectively. Proving that flatland pasture was likely to be close to the IPCC (2006) upper limit was a Choudrie et al (2008) dairy pasture data in the UK which had 74% digestibility, very close to the upper limit recommended by IPCC (2006) (Table 3.1).

Table 3.2. Digestibility of a range of UK feeds and their sources of information. Main category Class Feed

digestibility (DE%)

Reference

Sheep/cattle Feedlot animals with >90% concentrate as diet

75-85% IPCC (2006)

Pasture fed animals 55-75% IPCC (2006)Stock fed low quality forage 45-55% IPCC (2006)UK Dairy pasture 74% Choudrie et al (2008)Sheep set stocked on UK hill pastures

60-65% Russel (1971)

The gross energy content of feed types used in the model was kept as 18.45 megajoules of gross energy (MJGE)/kg dry matter. IPCC (2006) stipulated that this energy value was ‘relatively constant across a wide range of forage and grain-based feeds commonly consumed by livestock’.

Animal performanceIt was assumed that flock performance reflected industry average performance in the ‘base’ scenarios. Therefore average performance measures were applied to each area. In all sheep GHG models it was assumed that females were not mated until they were 18 months of age (i.e. two tooths). The range of traits considered are shown in Table 3.3.

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Selling policyLambs were assumed to be sold at a set carcase weight as opposed to a set age. It meant that improvements in growth traits would have a bearing on the number of days required to get the lamb to slaughter rather than influence carcase weight. The carcase weights for lambs were set at 16.8kg, 18.9 and 18.9kg for hill, upland and lowland lambs respectively. The respective lambs were assumed to attain carcase weight to live weight yields of 43%, 44.8% and 45% respectively (Appendix 2).

Table 3.3. Sheep traits altered in the Genetic GHG models and/or are included in the selection index modelAbbreviation Trait Name Description and UnitsLSB-D Litter size born-direct

(ewe prolificacy)Lambs per ewe lambing (1 percentage point increase)

LSR-D Litter size reared-direct (to weaning)

Lambs reared (weaned) per ewe lambing (1 percentage point increase)

Lamb survival-D Lamb survival-direct Lambs that survive from birth to weaning (1 percentage point increase)

MS- M Mature size-maintenance

Effect of MS on breeding ewe maintenance requirements(1 kg increase)

MS- R Mature size- replacements

Effect of MS on replacement energy requirements due to higher target live weight (1kg increase)

MS-Comb Mature size- combined Combined effect of the two MS traits above.SLAGE Offspring age at

slaughterDays post partum to slaughter (1 day reduction)

8WK-D 8 week live weight of offspring- direct

Kg live weight at weaning (1kg increase)

RFI-Grow. Residual feed intake of growing animals

1kg reduction in dry matter intake over the animals finishing period while maintaining production

RFI-Brd. Residual feed intake of breeding animals

1kg reduction in dry matter intake over the breeding animals year while maintaining production

MAT Litter size weaned Kg of lamb weaned per eweEwe Longevity Ewe Longevity Avg. years of age ewe dies or is culledSWT Scanning weight 1kg increase at scanning live weight at 21 weeksMD Muscle depth of

carcass at slaughter Millimetres

FD Fat depth of carcass at slaughter

Millimetre

Cmus CT scan muscle MillimetresCfat CT scan fat MillimetresCS Conformation score Score units (1-15)FECS Faecal egg count

StrongylesNumber of eggs

FECN Faecal egg count Nematodirus

Number of eggs

Footrot 1 unit change in average footrot score

Footrot score units (1-5)

LN WT Lean weight Kg of carcassSF Shear force test value

for lamb carcassKg force

Manure managementSheep that were housed indoors were assumed to kept on straw bedding (no slats) and therefore their manure was stored on a dry-lot. This caused no increase in the manure management methane emission factor (MCF in IPCC, 2006) from the 1% (at a ‘Cool’ 10-14 °C annual temperature) used for stock grazing pasture.

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3.3.4. Genetic GHG model sheep results

Tables 3.4-3.6 show the environmental impact (in terms of decrease/increase of GHG emissions) of changing selected traits in a hill, crossing and terminal sheep scenario respectively. The traits examined in each scenario are based on those currently used in respective breeding programmes as well as a range of “new” traits including efficiency traits. Positive values indicate a favourable effect (i.e. decrease) on the GHG emissions from a system and conversely negative value indicates favourable effect.

Table 3.4 and Table 3.5 show that ewe longevity is an important trait in terms of GHG emissions from hill and crossing (upland) sheep systems. The positive value of 10.95 kg CO2e/breeding ewe for hill sheep (Table 3.4) for improving ewe longevity by one year means that the overall GHG emissions from the hill system is reduced by approximately 11 CO2e/ewe and therefore a trait that would have a positive impact of the GHG budget for the hill system. It is important to note that environmental values for individual traits are not directly comparable because they are relative to the unit of expression of the trait. It can also be seen that mature size of animals in a hill scenario has an unfavourable impact of the overall GHG emissions as larger animals will eat more to maintain themselves and therefore have a larger environmental footprint. Across all 3 sheep systems improving lamb survival also has an unfavourable impact on GHG emissions from the system, based on the environmental footprint of rearing more animals from a given system. However this should be considered in tandem with the economic performance of the system.

The impact of feed efficiency on GHG emissions was also examined. This type of trait (e.g., residual feed intake) is not available in current genetic evaluation processed but will have an impact on the environmental footprint of the system and therefore a weight was calculated to help guide future developments of breeding goals.

Table 3.4. Greenhouse gas (GHG) values for hill sheep, with discounted genetic expressions and the GHG weights for each goal trait.Trait group Breeding

objective trait (units)

GHG value (-1*kg CO2e/breeding hill ewe/ unit change in trait)

Discounted genetic expression (per brd ewe)

GHG weight(-1*kg CO2e/brd ewe)

Maternal MAT (kg) 1.83 0.553 1.01Ewe longevity (/year)

10.95 0.044 0.48

Ewe size Mature size- maint. (kg)

-5.96 0.553 -3.30

Mature size- replacements (kg)

-2.79 0.123 -0.34

Mature size-combined

-3.64

Lambing LSR-D (%) -1.39 0.553 -0.77Lamb survival (%)

-1.60 1.55 -2.48

Growth/carcass 8WK-D (kg) 1.62 0.955 1.55SWT (kg) 6.67 0.955 6.37

Efficiency RFI- Growing 0.71 0.955 0.68

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animals (kg DM)RFI-Breeding animals (kg DM)

1.04 0.553 0.58

28

Table 3.5. Greenhouse gas (GHG) values for crossing (upland) sheep, with discounted genetic expressions and the GHG weights for each goal trait.Trait group Breeding objective

traitGHG value (-1*kg CO2e/breeding upland ewe/ unit change in trait)

Discounted genetic expression (per brd ewe)

GHG weight(-1*kg CO2e/brd ewe)

Ewe size Mature size- maint.(kg)

-4.89 0.677 -3.31

Mature size- replacements (kg)

-4.67 0.237 -1.11

Mature size-combined

-4.42

Lambing LSB-D (%) -0.88 0.677 -0.60Lamb survival- Direct (%)

-2.68 1.370 -3.67

Growth SLAGE (days) -2.50 0.863 -2.168WK-D (kg) 2.92 0.863 2.52

Efficiency RFI- Growing animals (kg DM)

1.09 0.863 0.94

RFI- Breeding animals (kg DM)

1.05 0.677 0.71

Ewe Longevity

Ewe longevity (/year) 14.89 0.183 2.72

Table 3.6. Greenhouse gas (GHG) values for terminal (lowland) sheep, with discounted genetic expressions and the GHG weights for each goal trait.Trait group Breeding objective

traitGHG value (-1*kg CO2e/breeding lowland ewe / unit change in trait)

Discounted genetic expression (per brd ewe)

GHG weight(-1*kg CO2e/brd ewe)

Efficiency RFI- Growing animals (kg DM)

1.16 0.381 0.44

Survival Lamb survival –D (%)

-2.09 0.490 -1.02

Growth 8WK-D (kg) 1.18 0.381 0.45SWT (kg) 10.66 0.381 4.06

3.4. Beef cattle

2.4.1. The herd

The UK beef industry has generally sourced heifers from cows crossed with beef breeds in the dairy industry. Due to the shift in the dairy herd towards more Holstein influence (a breed that is more specialised in milk production over beef) there has been a shift of beef herd managers toward producing more of their own replacement heifers. Disease issues have also enhanced the desire of many managers to breed their own replacements. Prior to the shift toward being more self reliant on replacements there was more emphasis on terminal traits. A single beef model was used to model GHG emissions associated with relevant beef genetic traits, but the flow of genes causing the trait changes was modelled separately to consider genetic improvement for traits in breeder herds supplying bulls to breed replacement heifers,

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versus improvements in traits by terminal sire breeders. The beef herd was assumed to be based in the hills/upland of the UK.

Parameters and performanceParameters for the beef models were aligned to Roughsedge et al (2005) and are depicted in Appendix 4. As in the sheep models, the number of breeding stock was limited to 100 (breeding cows) as opposed to being limited to a set quantity of energy.

As shown in Appendix 4, beef cows were assumed to have a mature weight of 600kg, with 2% involuntary deaths (i.e. deaths not from culls) and 3% barren each year. The calves were assumed to have an average age at weaning of 210 days with a 98% survival from weaning to slaughter.

Conversion factors which estimate the proportion of gross energy from cattle lost as methane ranged from 3% to 6.5% depending on whether the class of stock was fed indoors with more than >90% concentrates or outdoors on pasture. This is shown in Appendix 3.

When growth traits in cattle offspring were altered, no account of the subsequent increase in breeding cow/bull mature weight was made. This followed the same methodology as the sheep model whereby each trait was altered independently of other traits. Similarly, no account of seasonal fluctuations in mature weight was considered. Instead it was assumed that when cows or bulls gained ‘mature’ weight, they would reach homeostasis. In reality the annual fluctuation in beef cattle weight can be comfortably managed so that they lose up to 10% of their post weaning live weight (Morris, 2007). There is a net energy cost associated with this loss of body weight assuming it is gained back at a later stage in the year. As mentioned by Morris and Smeaton (2009) it is approximately 25 MJME net loss per kilogram of body weight lost. Therefore this model does not take into account the energy hence GHG cost of seasonal fluctuations in mature animal weight. It was assumed fluctuations would be the same between scenarios with different genetic changes.

2.4.2. Environmental influences

TemperatureBeef cattle were not assumed to be exposed to the same average UK climate as that described in the sheep section (Section 2.3.2). Cattle spent half the year indoors (Appendix 4). Therefore it was assumed that their maintenance energy requirements did not have to increase to account for the cold winter temperatures.

As in the sheep models, the beef cattle were assumed to be exposed to ‘Cool’ annual average temperatures (10-14 ºC) for the purposes of quantifying the manure management coefficient. This meant that manure management coefficients for cattle managed on pasture were 1%. For cattle which had their manure managed from indoor housing this coefficient was 3.9% and is explained further later (manure management).

2.4.3. Management

Pasture management and qualityIt was assumed that the cattle grazed upland pastures/sub-prime pasture. According to Common et al. (1990), hill pastures had pasture production figures of approximately 4515kg DM/ha pa (Appendix 4). They were assumed to be held indoors for half the year, where they were fed a mixture of silage and hay over this

30

period. The average crude protein percentage in the annual diet was 18%. The annual average for pasture quality was 18.45 MJGE/kg DM following recommendations by IPCC (2006) that the feed energy value was ‘relatively constant (at 18.45 MJGE/kg DM) across a wide range of forage and grain-based feeds commonly consumed by livestock’.

Animal performance Beef cattle were assumed to have a performance on par with the UK average. For fertility for instance, it was assumed that 2 year old heifers were mated to bulls, but consequently only 50% of heifers mated actually weaned a calf to follow the assumptions made by Roughsedge et al (2005). All other cows were mated, and 85% of 3 year old cows were assumed to wean a calf. Ninety percent of cows 4 years or older were assumed to wean a calf (Appendix 4). The number of dry cull cows came from calculating the number of cows aged over three years of age that did not produce a calf in two consecutive years. The range of traits considered in beef breeding current beef index and other correlation traits are shown in Table 3.7.

Table 3.7. Traits altered in the cattle GHG model and/or are included in the selection index modelAbbreviation Trait Name Description and UnitsMW-Maint. Mature weight-

Maintenance1 kg increase of cow live weight

MW-Replace. Mature weight- Replacements

1 kg increase in target live weight for replacements

MW-Comb Mature weight- Combined

kg (combining the above two phenotypes)

CW Carcass weight Kg carcass weightWT200-M Weaning weight at

200days of agekg live weight

WT-400-M Weight at 400 days of age

kg live weight

BWT-D/M Birth weight of offspring either direct or maternal

kg live weight

CI Calving interval Weaning % of cows reduces by 1 percentage pointAFC Age to first calving 1 percentage point increase in calving percentage for

heifers that are exposed to calve at 2yrs that calve at 3 yrs

RFI-breed ani. Residual feed intake 1kg DM reduction in the dry matter intake of breeding animals each year while maintaining production

RFI- grow. Residual feed intake 1kg DM reduction in the dry matter intake of growing animals each year while maintaining production

WSV-D Wean survival (calf survival from birth to wean increase)

1 percentage point increase in survival

FD Fat Depth MillimetresMD Muscle Depth MillimetresCFS Carcass fat score Units (1-15)CCS Carcass condition

scoreUnits (1-15)

GL-D/M Gestation length- direct or maternal

Days

CD-D/M Calving difficulty- direct or maternal

CD units

LS Lifespan Years at time of disposalSF Shear Force KgBSV Birth survival 0 or 1DS Docility score (1-6)

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MSC Muscle score (1-15)

Selling policyAs with the sheep models, the beef management system aimed to produce offspring at set carcase weights, not age. Three hundred and twelve kilograms was set as the target carcase weight. With pre and post weaning live weight gains assumed to be 0.8 and 0.75kg/day respectively this achieved the desired slaughter weight in 733 days of age (or 24 months).

The current economic index used for selection of beef cattle in the UK (Amer et al. 1997) uses economic weights from a model which optimises slaughter endpoint. However, economic values were found to be very similar at optimum constant age, and constant carcase weight endpoints.

Manure managementCattle that were housed indoors for any period of time were assumed to have their manure managed as a combination of farm yard manure and liquid slurry. A survey from Smith et al (2001) concluded that for Wales and England, 82% of manure from beef while indoors was managed as farm yard manure (FYM). These cattle were considered to have been housed indoors in straw bedded loose yards whereas 18% of cattle manure was concluded to come from cattle managed in indoor cubicles or kennels producing slurry.

The combined Wales and England survey results from Smith et al (2001) were extrapolated to be the UK averages. This had a bearing on the manure management methane emission factors (Manure coefficient factor-MCF in IPCC, 2006) which related to the quantity of methane released from the manure to temperatures and how it was handled. For example if ‘Cool’ (10-14 °C) annual temperatures were assumed for the UK, the IPCC (2006) MCF factor for manure management of 1% was used for manure managed in a drylot (straw bedded and loose yards). For the same temperature range a MCF of 17% is recommended by IPCC (2006) for manure managed as liquid slurry. The overall cattle MCF percentage was given as a weighted average of 82% of manure at 1% MCF and 18% of manure at 17% MCF, equivalent to 3.9%.

3.4.4. Genetic GHG model beef results

Tables 3.8 and 3.9 show the environmental impact (in terms of decrease/increase of GHG emissions) of changing selected traits in a beef maternal and terminal herd respectively. The traits examined in each scenario are based on those currently used in respective breeding programmes as well as a range of “new” traits including efficiency traits. Positive values indicate a favourable effect (i.e. decrease) on the GHG emissions from a system and conversely negative value indicates favourable effect.

Table 3.8 shows that improving beef cow fertility, both calving interval and age to first calving, has a beneficial impact on environmental footprint of the system. This is a result of reducing the number of empty days due to poor fertility. As with sheep, increasing the mature sheep of breeding animals has an unfavourable impact of the environmental footprint of beef maternal system, due to the larger maintenance costs of bigger animals.

In both beef scenarios the feed efficiency, as defined by residual feed intake (RFI), has a favourable effect on reducing GHG emissions. Although RFI is not considered

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in current breeding goals the Defra project (IF0149) is exploring how such a trait could be considered in beef breeding programmes.

Table 3.8. Greenhouse gas (GHG) values for maternal beef cattle, with discounted genetic expressions and the GHG weights for each goal trait.Trait group Breeding

objective traitGHG value (-1*kg CO2e/breeding cow/ unit change in trait)

Discounted genetic expression(per brd cow)

GHG weight(-1*kg CO2e/brd cow)

Maternal Calving interval (days)

7.46 0.774 5.77

Age first calving 3.85 0.141 0.54WT200-M 8.80 0.654 5.76

Cow size Mature weight- maint. (kg)

-0.878 0.774 -0.68

Mature weight- replacements (kg)

-1.89 0.141 -0.27

Mature weight-combined

-0.95

Growth/carcass Carcass weight (kg)

12.26 0.680 8.34

Efficiency RFI-Brd ani. 0.43 0.774 0.33RFI-Grow. ani 0.38 0.680 0.26

Table 3.9. Greenhouse gas (GHG) values for terminal beef cattle, with discounted genetic expressions and the GHG weights for each goal trait.Trait group Breeding

objective traitGHG value (-1*kg CO2e/breeding cow/ unit change in trait)

Discounted genetic expression(per brd cow)

GHG weight (-1*kg CO2e/brd cow)

Growth/carcass Carcass weight (kg)

12.26 0.430 5.27

Efficiency RFI- Growing animals

0.38 0.430 0.16

Survival WSV-D -16.54 0.654 -10.82

3.5. Dairy cattle

3.5.1. The herd

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 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.

Parameters and performanceThe basic biological and herd parameters are given in Appendix 5, most of which are based on the study of Stott et al (2005), which involved a level of specialist and industry validation of the assumptions. These figures were chosen to provide a consistent set of figures thought representative of UK dairy farming practice.

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Where possible, parameters from the papers that described the development of the respective selection index were used. Any absences of required parameters in the respective papers were filled in by using one of the other selection index papers.

The model parameters used by Stott et al (2005) and IPCC Tier II/III methodologies (IPCC, 2006) were used to model the CH4 (enteric fermentation and manure management) and N2O (manure management) emissions from the whole farm system (young stock and milking herd). Under IPCC framework N2O emissions due to nitrogen excretion when cows are grazing should be reported in the agricultural soils of the inventory framework. This study, however, will include these emissions as it accounts for a large proportion of the total nitrogen excreted by the dairy system and therefore GHG emissions.

When examining the impact of improving milk yield on GHG emissions it was assumed that overall herd milk output would not alter, as would occur under quota scenario. This would result in a fewer numbers of milking animals, and followers, required to meeting a given level of production output.

3.5.2. Environmental influences

TemperatureDairy cattle were not assumed to be exposed to the same average UK climate as that described in the sheep section (Section 2.3.2). Cattle spent half the year indoors (Appendix 5). Therefore it was assumed that their maintenance energy requirements did not have to increase to account for the cold winter temperatures.

As in the sheep models, the dairy cattle were assumed to be exposed to ‘Cool’ annual average temperatures (10-14 ºC) for the purposes of quantifying the manure management coefficient. This meant that manure management coefficient for dairy cows at grass was 1.5%. For cattle which had their slurry managed from indoor housing this coefficient was averaged at 17% to represent the average values for the range of slurry management systems on dairy farms (see manure management section).

3.5.3. Management

Feed managementIt was assumed that the dairy cows were grazed for approximately half the year on lowland pastures. Cows were maintained indoors on a mixed diet of grass silage and concentrates for the remainder of the year. The followers were assumed to follow a similar management system. The average crude protein percentage in the annual diet was 18%. The annual average for pasture quality was 18.45 MJGE/kg DM following recommendations by IPCC (2006) that the feed energy value was ‘relatively constant (at 18.45 MJGE/kg DM) across a wide range of forage and grain-based feeds commonly consumed by livestock’. The impact of the different diets in terms of GHG emissions were taken from the LCA study of Williams et al (2006).

Animal performance The dairy herd were assumed to have a performance on par with the UK average and as described by Stott et al (2005) and further details are given in Appendix 5. The range of traits considered in beef breeding goals today and traits examine here in not yet considered in breeding goals is shown in Table 3.7.

Manure management

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The manure management of dairy herds was not included in the estimation of the REVs as described by Stott et al (2005). Therefore, emissions from manure storage and production at grass were estimated based on the days at grass and indoors of the model and assumptions on manure output and storage from IPCC and Prevention of Environmental Pollution from Agricultural Activity (PEPFAA, 2005). Greenhouse gases from manure were calculated based on volume produced from each of the livestock categories kept on the farm (e.g., dairy cows and followers).

Table 3.10. Dairy traits considered in the breeding goal Trait group

Goal trait Index trait Abbreviation

Production Milk yield (1kg ↑) MILK 305d milk yield (kg)Milk fat yield (1kg ↑) FAT 305d milk fat yield (kg)Milk protein yield (1kg ↑) PROT 305d milk protein yield

(kg)Longevity Lifespan (+1 lactation) LS Lifespan (no. of lacns

survived)Health Lameness (1% ↓ in

incidence)LAME Locomotion score

(linear scale)LOCO

Feet & legs score (linear scale)

F&L

Mastitis (1% ↓ in incidence) MAST Somatic cell count (count)

SCC

Mammary score (linear scale)

MAM

Fertility Calving interval (+1 day) CI Calving interval (lacn 1-2, days)

Conception (1% ↑ in conception rate)

CR Non-return rate 56 days (0/1)

NR56

3.5.4. Genetic GHG model dairy results

Figure 3.1 shows that overall the dairy system produced approximately 1150 t CO2 e per annum. The largest proportion of GHG emissions can be attributed to the milking herd, making up approximately 50% of the total emissions (including N2O and CH4

from enteric fermentation and manure management).13% of the emissions are due to following herd. Over 37% of the emissions can be attributed to the GHG emissions associated with the production of diet for all categories of animals throughout the year.

Tables 3.11 show the environmental impact (in terms of decrease/increase of GHG emissions) of changing selected traits in a dairy herd. The range of traits that this could be examined in the dairy herd was limited due to trait definition within the dairy herd. For example, the trait “lifespan” incorporates culling for all non-production traits, including health and fertility. This means that the benefit/disbenefit of improving health and fertility in the dairy herd on overall GHG emissions due to reduction in culling rate is encapsulated in the trait and therefore weight on lifespan.

Table 3.11. Greenhouse gas (GHG) values for dairy cattle,Trait Group GHG value (-1*kg

CO2e/cow/unit change in trait)

Methane value (-1*kg CO2e/cow/unit change in trait)

GHG value (-1*kg CO2e/kg milk/unit change in trait)

Production MILK (kg) 0.681 0.316 0.079FAT (kg) -6.166 -3.227 -0.719

Longevity Lifespan (lactations) 68.91 32.84 7.96

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Fertility Conception 3.382 1.373 0.615

0

50

100

150

200

250

300

350

400

450

500

Enteric CH4(grass)

Enteric CH4(indoors)

Manure CH4(grass)

Manure CH4(indoors)

N2O Diet

GHG source

t CO

2eq

Young Cows

Figure 3.1. The amount of GHG emissions due to methane (CH4) and nitrous oxide (N2O) from young stock and milking cows in the base dairy herd from the Genetic GHG model, including the total global warming potential of the diet.

Overall, improving milk yield in the dairy herd has a favourable impact on GHG emissions as fewer cows and followers are required to meet the same production level. The average lifespan in the base dairy scenario was 3.5 lactations. Improving this by one lactation (4.5 lactations) had large and favourable impact on GHG emissions from the dairy herd, reducing them by 69 kg of CO2e per milking cow. Improving milk fat yield had an unfavourable impact on the GHG emissions from the herd as increasing the percentage milk fat means that more feed energy is required to produce that milk fat and therefore overall system emissions are increased. Improving conception rate was examined by improving the conception rate in both virgin heifers and mature cows and as such assumes a perfect correlation between the two. As yet virgin fertility is not a separate trait in dairy breeding goals, however future developments may allow for such a trait to be included in the routine genetic evaluations.

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4. Impact of altering breeding goals on biological, economic and environmental performance of ruminant production systems

4.1. Methodology to predict expected responses to differing ruminant breeding goals

4.1.1. Selection index methodology

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). Descriptions of the current selection indices in sheep, beef and dairy are presented in Section 2.

Selection index theory (Hazel, 1943) was used in sheep, beef and dairy examine the consequences of selection on current and alternative future breeding goals. Models of selection in the alternate sheep and beef breeding scenarios and 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.

Sheep and BeefThe overall breeding goals for current selection indices in beef and sheep are based optimising the economic performance. The respective index traits and weights for this index were given earlier (Table 2.2 and 2.3). These indices were used as the base index for comparison with different breeding goals to quantify their impact.

Expected responses to selection of alternative breeding goals, with differing weights were examined by building a selection index framework suitable for each of the beef and sheep scenarios of interest. Phenotypic and genetic parameters between the traits in the breeding goal, selection index and correlated traits of interest were collated from estimates from previous studies and collated from literature as part of a detailed literature review in Defra project IF0149. Responses to selection on the alternative indices were calculated (Hazel, 1943). Unlike the dairy selection index model (discussed later) the beef and sheep models examined expected responses due to genetic improvement in males and females separately. This is to account for the different generation intervals, sources of information (i.e. number of relatives with recorded information is a given trait) for different traits and selection intensities that can be placed on different routes of genetic improvement in the pedigree of animals. The information on the selection intensity, generation intervals, and numbers of relatives with recorded information in a given trait were calculated from data from the UK genetic evaluations and were estimated based on these data. Further information on the collation of these data are given in the report from the Defra study IF0149.

DairyThe 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. The respective index traits and weights for this index are given earlier (Table 2.1). 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 dairy selection index framework. Phenotypic and

37

genetic parameters between the traits in the breeding goal, selection index and correlated traits of interest were collated from previous studies (Stott et al, 2005; Wall et al., 2003 & 2006). Responses to selection on the index were calculated (Hazel, 1943). Annual returns were calculated based on a 0·22 standard deviations change in the aggregate index (Robertson and Rendel, 1950). This value approximates selection response in ‘typical’ four pathway dairy cattle breeding schemes. Generation intervals were assumed to be 6.5 years for sires and 5 years for dams. Following a progeny test scheme example progeny test candidate bulls were expected to have, on average, 75 daughters that across all traits that contribute to the first breeding value for that bull. The annual genetic improvement made comes about by the availability of new, genetically superior, semen from young bulls. Although bulls may go on to have thousands of daughters over their “breeding” lifetime (could be many years for popular bulls via widespread AI) the initial impact on the genetic superiority of the dairy population will be when they are first used.

4.1.2. Discounted genetic expressions

Selection index theory is based on the concurrent selection for multiple traits weighted by their relative importance (Hazel, 1943). However, the genetic expression of traits in an index and breeding goal may be at different times over the lifetime of an animal, and/or the lifetime of their offspring. For example, maternal traits of breeding ewes/cows are likely to impact on more than one offspring. This is particularly relevant in beef and sheep where large differences in genetic expressions occur between those animals bred to produce slaughter offspring and those bred to produce replacements. Therefore, the weighting factors on traits within a selection index should reflect the economic benefit of genetic change in the trait simultaneous with the frequency and timing of expressions of the trait over multiple generations. McClintock and Cunningham (1974) suggested the use of cumulative (total standard) discounted expressions (CDE) as a means of discounting to a pre-defined time. Cumulative discounted expressions may be calculated as the sum of all timing and frequency of expressions of a trait over multiple generations originating from one initial mating. These have been applied to both sheep (Amer, 1999) and beef (Amer et al., 2001) production systems and these techniques have be utilised in this project to derive index weightings factors for the environmental weights derived for beef and sheep. It should be noted that CDE are less of an issue in dairy cattle breeding as majority of genetic improvement originates from the selection of dairy bull sires and their value in terms of dairy daughter performance (e.g., milk production, female fertility, lifespan). However, this methodology may need to be applied when considered newer traits such as calving ease and carcass value of dairy cattle.

SheepSimilar techniques were applied to the sheep scenario to quantify the economic value of genetic superiority in individual rams or ewes accounting for the impact of crossing sire (terminal sheep sires, Appendix 6), replacement breeding policies and traits expressed in ewes or lambs at different ages. The subsequent discounted genetic expressions (Table 3.4-3.6) can be multiplied by the economic or environmental value of a change in a specific trait when weighting selection indexes.

Previous index economic weights in sheep have utilised discounted genetic expressions to scale economic weights before they have been applied as index weights. In this study for hill sheep in particular, the economic weights were altered to ensure they produced comparable results with the other sheep index weights. For example, crossing and terminal sheep had economic weights that were a multiplication of the economic values in £ per breeding ewe and DGE (discounted genetic expressions) set at a 7% discount rate over a 20 year time horizon. Hill

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sheep economic weights were originally based on multiplying the economic weight per 100 breeding ewes with a DGE at 5% discount rate over a 15 year time horizon (Conington et al. 2001). To ensure results could be compared to other studies, the original hill sheep economic values were used. However, these were then multiplied by DGE similar to those used in the crossing and terminal sheep indexes (i.e. with a 7% discount rate over a 20 year time horizon).

BeefBecause economic values are expressed per unit change in traits that differ in the timing and frequency of expression in commercial suckler beef production, it is necessary to modify them before applying them in a multi-trait selection index. One method of doing this is to compute discounted genetic expressions coefficients (e.g. Amer et al., 1999 and Amer et al., 2001). The equations presented by Amer et al. (2001) were used with cow survival rates as used in the calculation of the longevity economic value (EV). It was assumed that 0.5 of two year old cows produce a live calf that survives until weaning, 0.85 of three year olds cows and 0.9 for cows from age 4 to 11 years. The survival of calves from weaning until slaughter, or replacement age, was assumed to be 0.98. An annual discount rate of 0.07 for economic weights and 0.02 for environmental weights was used to penalise traits expressed with delays of 1 or more years relative to the expression of other traits. As a result, expected numbers of discounted trait expressions of a sires genes at birth (XTB) and slaughter (XTS) per calf born that is destined for slaughter were derived. Discounted genetic expressions of a sires genes in his daughters at annual calvings (XRA), weaning of their calves (XRW), replacement heifer age (XRH) and at culling (XRC) per female calf born and destined to become a replacement. The derived values are presented in Table 4.1. These values should be multiplied by a factor of 2 when the index is to be expressed on an estimated breeding value (EBV) basis, as opposed to predicted transmitting ability (PTA) basis.

Table 4.1. Table of computed discounted genetic expressions coefficients for bulls genes in terminal and replacement female descendents.Coefficient Abbreviation ValueTerminal (per progeny slaughtered)Birth XTB 0.5Slaughter XTS 0.39

Replacement (per progeny replacement)Annual cow XRA 3.30Annual cow by weaned calf XRW 2.79Heifer replacement XRH 0.71Cull cow XRC 0.34

4.1.3. Environmental weights in ruminant selection indices

Section 2 describes the current set of index weights used currently in beef and sheep index. These weights have already been discounted to consider the genetic expression for each of the traits. This study has created a set of alternative weights that incorporate the environmental impact of different traits. Section 3 describes the change in overall environmental impact of changing individual traits in sheep, beef and dairy systems. 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 breeding goals in sheep, beef and dairy populations.

39

The environmental weights that were calculated in Section 3 for sheep, beef and dairy can be used to calculate a set of index weights that shift the breeding goal from that of the current economic goal to an environmental goal. In the case of beef and sheep indices, the environmental values assigned to each of the underlying traits are weights by the discounted genetic expression weight shown in section 3. These environmental weights are expressed in 2 forms for sheep and beef, per breeding female (ewe and cow) and per kg of meat output. In dairy three sets of environmental breeding goals where derived, looking at the environmental impact in terms of per dairy (milking) cow, per kg of milk as well as the GHG emissions associated with enteric fermentation per dairy cow. This latter index scenario would represent an environmental index if the only GHG emissions related to methane production from the animals in the dairy herd (milking cows and followers) were considered rather than the wider lifecycle implications of changing traits in the production system.

It should be noted that GHG values presented in Section 3 were multiplied by DGE calculated with 2% discount rates rather than the 7% discount rate applied to economic values. The 2% discount rate was applied to environmental values as it was assumed that interest would not be charged on emissions. It is assumed that the emissions savings/costs will be close to today’s value in 5-10 years time. However, this will depend on the trajectory of world carbon emissions (Price et al. 2007).

4.1.4. Merging environmental and economic weights in ruminant selection indices

Taking account of societal views in an economic framework of a selection index can be difficult as they are a combination of market and non-market attributes (Olesen et al., 1999). Non-market goods are those that typically cannot be transacted in conventional markets but whose provision increases social welfare. Many non-market goods have public good characteristics, meaning that the public sector (i.e. government) often has to intervene to address the so-called market failure in their adequate provision. That is, while some attributes provide a public good, there is by definition no corresponding monetary return from their provision.

Section 3 uses farm modelling to calculate the indirect environmental impact of traits in terms of their Global Warming Potential or carbon equivalents as a result of a unit change in a trait (e.g. fertility). These weightings will be used as stand-alone selection index weightings ("relative environmental values") to create an environmental selection index. However, as with all indices using weightings other than those based on expected market values, such indices may produce suboptimal profitability for producers.

From a public perspective (e.g. government) the economic appraisal of GHG emissions is complex, and mitigation options must compare the costs associated with that option with the benefits in terms of emissions damage avoided. The latter is approximated by the shadow price of carbon (SPC), which is derived from the best estimate of the present value of damages associated with a tonne of GHG emission in carbon dioxide equivalents (CO2 e, Price et al, 2007). The value of the SPC is the focal point of much research in the economics of climate change around the world. Figure 4.1 shows the value of SPC as estimated by the UK Department of the Environment, Food and Rural Affairs have defined (Defra, 2007). The SPC is being used across UK government departments to help appraise suggested public mitigation policies. It could also conceivably become the basis of a publicly-backed breeding initiative aimed at GHG mitigation from livestock. Figure 4.1 shows that the value of SPC is rising through time, reflecting the increasing marginal damage of a tonne of GHG when added to a growing stock of atmospheric GHGs. This SPC is

40

useful because it provides a benchmark against which to judge the cost efficiency of mitigation options as well as providing a monetary value for GHG emissions.

However, the SPC is not the only prevailing carbon “price”. Since 2005 the European Union Emissions Trading Scheme (ETS) has been in existence for the transaction of carbon allowances in a restricted or capped market between holders of credits and those that need to pay for them as a cheaper alternative than mitigating their emissions through some technological add-on or production alteration. In theory, the SPC and the ETS prices should converge (Stern, 2007). This is not proved here, but the basic point is that either provides a shadow value that could ultimately be built into a breeding index. By way of motivating this development suppose, not inconceivably as in New Zealand, that agriculture is forced into an emissions trading scheme and that farmers must hold valuable permits either through initial allocation or by purchasing in the ETS. Such a policy move will immediately move GHG mitigation traits from a public to a private breeding objective. By extension, the prevailing emissions price becomes the relevant economic weight that should be incorporated in any breeding index that includes mitigation potential.

0

10

20

30

40

50

60

70

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

£/t C

O2

eq

Figure 4.1. Shadow price of carbon in £GBP per tonne of carbon dioxide equivalents (£/t CO2 e) to 2050 based on 2007 prices and a 2% per annum increase (Defra, 2007)

An alternative set of selection index weights were derived for ruminant systems by combining the current set of economic index weights with the environmental weights using the ETS price for carbon and a range of SPC. Using a range of carbon prices allows us to explore the impact of different mechanisms of managing carbon reductions on farm. The hybrid index weights (Eco+ CO2£) was a combination of the carbon cost in £ of CO2e for each unit trait change expressed per breeding cow (GHG1) added to the economic weight. There is considerable uncertainty surrounding the price of carbon on a £/t CO2e basis. Therefore 4 different prices were selected:

41

CO2£a: £12/t CO2e- the approximate 2009 median price for EUA (the European allowance) carbon units (Bloomberg New Energy Finance, 2009)

CO2£b: £26.50/t CO2e- the 2009 Shadow price of Carbon (SPC) from Price et al. (2007)

CO2c: £32.90/t CO2e- the 2020 SPC from Price et al. (2007) chosen to represent a future price of carbon in 2-3 generations in sheep, beef and dairy. It is usual to forecast values when deriving index weights as the results of a selection decision today are expressed/realised in the future.

CO2£d: £100/t CO2e- a worst case scenario for price of carbon to ensure an adequate range for the results.

4.2. Results

For simplicity, the results are presented in a similar manner across sheep, beef and dairy. The set of selection index weights used in each of the breeding scenarios (hill, cross and terminal sheep, maternal and terminal beef and dairy) will be presented in the first table of each section. These weights include the current selection index economic weights, a newly derived set of selection index environmental weights (i. expressed per breeding ewe/cow for sheep/beef and milking cow for dairy, GHG1 and, ii. per kg of meat/milk product output, GHG2) and the combined set of selection index economic and environmental weights (using the 4 prices for carbon CO2£a – CO2£d).

The next table will present the expected responses to selection in the biological traits on the different selection index weights. These responses will be expressed per annum and in the relative units of the traits. For presentation issues some additional trait responses are given in the Appendices to this report.

The final table will present the expected overall responses to selection on the different selection index weights. These overall responses will include the overall economic response based on the economics of the current selection indices, the environmental response per breeding animal and per kg product and the environmental economic response reflecting the economics of current indices combined with carbon economics at the 4 price levels.

4.2.1. Hill sheep results

Table 4.2 shows the set of 7 selection index weights that were used in the selection index model for hill sheep and Table 4.3 shows the expected annual responses in the traits in the selection index with each of the sets of index weights. 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. 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 4.3 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

42

(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 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 4.2. Selection index weights used in hill sheep including the current set of economic weights (Current), environmental weights expressed per breeding ewe (GHG1) and per kg of lamb meat (GHG2) and combined economic and environmental weights at four carbon prices (Eco+CO2£a-d) (Please refer to Table 3.3 for detailed descriptions of traits and units)

Breeding objectives and selection index weightsCurrent

(£)GHG1

(kg CO2e)

GHG2(kg

CO2e)

Eco+ CO2£a

(£)

Eco+ CO2£b

(£)

Eco+ CO2£c

(£)

Eco+ CO2£d

(£)Trait namesCfat -0.14 0 0 -0.14 -0.14 -0.14 -0.14Cmus 0.55 0 0 0.55 0.55 0.55 0.55MS -0.05 -3.64 -0.33 -0.10 -0.15 -0.17 -0.42MAT 0.19 1.01 0.09 0.20 0.22 0.22 0.29LSR-D 0.10 -0.77 0.15 0.09 0.07 0.07 0.028WK- Direct 0.38 1.54 0.14 0.40 0.42 0.43 0.54RFI-Lambs 0 -0.67 -0.06 -0.01 -0.02 -0.02 -0.07RFI-Ewes 0 -0.57 -0.05 -0.01 -0.02 -0.02 -0.06Ewe longevity 0 0.48 0.39 0.01 0.01 0.02 0.05Lamb survival, direct

0 -2.48 0.48 -0.03 -0.07 -0.08 -0.25

Table 4.3. Index and correlated trait responses for hill sheep when seven different breeding objectives were selected. (Please refer to Table 3.3 for detailed descriptions of traits and units).

Breeding objectives and trait responses (in trait units pa.)Current GHG1 GHG2 Eco+

CO2£aEco+

CO2£bEco+

CO2£cEco+

CO2£dTrait namesCfat 0.028 -0.071 -0.070 0.011 -0.013 -0.021 -0.061Cmus 0.174 -0.140 -0.135 0.148 0.100 0.080 -0.059MS 0.558 -1.227 -1.200 0.267 -0.138 -0.287 -1.019MAT 0.083 -0.114 -0.109 0.058 0.020 0.005 -0.079LSR-D 0.019 -0.022 -0.021 0.014 0.007 0.004 -0.0148WK- Direct 0.316 -0.123 -0.119 0.304 0.259 0.235 0.032RFI-Lambs 0.033 0.003 0.005 0.036 0.037 0.036 0.021RFI-Ewes 0.000 0.000 0.000 0.000 0.000 0.000 0.000Ewe longevity 0.003 0.006 0.043 0.005 0.006 0.007 0.008Footrot 0.002 -0.004 -0.004 0.001 0.000 -0.001 -0.003FECS -0.019 0.005 0.005 -0.019 -0.017 -0.016 -0.004FECN 0.021 -0.005 -0.005 0.021 0.019 0.018 0.005Lamb survival, direct

0.001 0.001 0.001 0.001 0.001 0.001 0.000

Table 4.4 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

43

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.

If the cost of carbon were to be included in the current economic index (Eco+ CO2£a) there would be no significant difference in overall response at the 2009 EUA carbon price (£12/t CO2e). Increasing the price of carbon to £26.50 (Eco+ CO2£b) would lead to a 3 pence per breeding ewe per annum reduction in overall economic response, reducing the overall annual economic response to £0.17/ewe. However, it would result in a change in the amount of GHG being produced from 1.4kg CO2e more being produced each year (using the current index) to reducing emissions by -0.89kg/breeding ewe pa, an 0.3% reduction in emissions per breeding ewe. This indicates a set of weights with an intermediate and favourable benefit in terms of economic and environmental performance compared to selection on profit only (Current) and environment only (GHG1 and GHG2)

It should be noted that the price of carbon would have to be £100/t CO2 to have a significant impact on changing the breeding goal of hill sheep away from current economic goal to begin to compensate farmers for losses in profit.

Table 4.4. Summary of hill sheep annual selection response outcomes over the seven breeding objectives

Breeding objectives and overall responses per annum.Selection index weights used

Units Current GHG1 GHG2 Eco+ CO2£a

Eco+ CO2£b

Eco+ CO2£c

Eco+ CO2£d

Current index £/ewe £0.20 -£0.08 -£0.07 £0.19 £0.17 £0.15 £0.02GHG reduction per ewe

kg CO2e/ ewe

1.50 -4.18 -4.11 0.48 -0.89 -1.38 -3.68

GHG reduction per kg product produced

g CO2e/ kg product

129.91 -376.52 -383.16 38.01 -85.47 -129.65 -333.54

EU ETS 2009 carbon price (£12/t CO2e)

£/ewe £0.18 -£0.02 -£0.02 £0.19 £0.18 £0.17 £0.07

UK SPC 2009 (£26.50/t CO2e)

£/ewe £0.16 £0.04 £0.04 £0.18 £0.19 £0.19 £0.12

UK SPC 2020 (£32.90/t CO2e)

£/ewe £0.15 £0.06 £0.06 £0.18 £0.20 £0.20 £0.14

44

Worst case (£100/t CO2e)

£/ewe £0.05 £0.35 £0.34 £0.15 £0.26 £0.29 £0.40

4.2.2. Crossing (upland) sheep results

Table 4.5 shows the set of 7 selection index weights that were used in the selection index model for crossing (upland) sheep and Table 4.6 shows the expected annual responses in the traits in the selection index with each of the sets of index weights.

Table 4.5. Selection index weights used in crossing (upland) sheep including the current set of economic weights (Current), environmental weights expressed per breeding ewe (GHG1) and per kg of lamb meat (GHG2) and combined economic and environmental weights at four carbon prices (Eco+CO2£a-d) (Please refer to Table 3.3 for detailed descriptions of traits and units).

Breeding objectives and selection index weightsCurrent

(£)GHG1

(kg CO2e)

GHG2(kg

CO2e)

Eco+ CO2£a

(£)

Eco+ CO2£b

(£)

Eco+ CO2£c

(£)

Eco+ CO2£d

(£)Trait namesSLAGE -0.07 -2.16 -0.10 -0.09 -0.13 -0.14 -0.28CONF15 0.55 0 0 0.55 0.55 0.55 0.55LNWT 5.87 0 0 5.87 5.87 5.87 5.87MS -0.50 -4.42 -0.21 -0.55 -0.62 -0.65 -0.94LSB- Direct 4.70 -0.59 0.06 4.69 4.68 4.68 4.64RFI-Lambs 0 -0.94 -0.05 -0.01 -0.02 -0.03 -0.09RFI-Ewes 0 -0.71 -0.03 -0.01 -0.02 -0.02 -0.07Longevity 0 2.72 0.91 0.03 0.07 0.09 0.27Lamb survival, direct

0 -3.67 0.27 -0.04 -0.10 -0.12 -0.37

Selecting on environmental weights (GHG1) for crossing sheep (Table 4.6) resulted in the profit trait response for slaughter age (SLAGE) to reduce from -1.92 days (for the current index) to -5.2 days. Improving the expected rate of reduction in slaughter age is favourable in terms of production efficiency as it means that lambs are getting to slaughter in fewer days post partum. However this comes at the cost of lower response in lean weight of a carcass (LNWT) which had a negative (-0.04 kg) response when selecting on an environmental index (GHG1) compared to the current index which had a positive (0.06 kg) response.

Table 4.6. Index and correlated trait responses for crossing (upland) sheep when seven different breeding objectives were selected (Please refer to Table 3.3 for detailed descriptions of traits and units).

Breeding objectives and trait responses (in trait units pa.)Current GHG1 GHG2 Eco+

CO2£aEco+

CO2£bEco+

CO2£cEco+

CO2£dTrait namesSLAGE -1.922 -5.214 -5.174 -2.769 -3.463 -3.685 -4.670CONF15 -0.012 -0.005 -0.006 -0.011 -0.011 -0.010 -0.008LNWT 0.054 -0.046 -0.045 0.038 0.023 0.017 -0.013MS -0.569 -0.549 -0.541 -0.616 -0.641 -0.646 -0.635LSB- Direct -0.003 -0.004 -0.004 -0.003 -0.004 -0.004 -0.004RFI-Lambs 0.001 -0.037 -0.036 -0.007 -0.014 -0.016 -0.028RFI-Ewes 0.000 0.000 0.000 0.000 0.000 0.000 0.000Longevity -0.010 -0.020 -0.009 -0.013 -0.015 -0.016 -0.019Footrot -0.006 0.001 0.000 -0.006 -0.005 -0.004 -0.002FECS 0.008 0.001 0.001 0.007 0.006 0.006 0.004FECN 0.004 -0.001 -0.001 0.003 0.003 0.002 0.001

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Lamb Survival-Direct

-0.001 -0.001 0.000 -0.001 -0.001 -0.001 -0.001

Crossing sheep followed similar trends to hill sheep in that the highest overall economic responses were calculated with the current index weights at £0.72 /breeding ewe pa (Table 4.7). Unlike in hill sheep, all index weights, economic and environmental, were predicted to have a favourable overall economic (£0.35/ewe/annum to £0.72/ewe/annum) and environmental (reduction of GHG emissions by 6.6 kg CO2e/ewe/annum to 13.7 kg CO2e/ewe/annum) response. Selection on an environmental index, therefore, is expected to reduce GHG emissions, cumulatively, of 2.9% per ewe per annum, compared to 1.4% reduction with selection on the current index

When the cost of carbon was taken into account in the index weights, only a small reduction (5 pence per ewe per annum.) in overall response was calculated at a SPC of £26.50. However, adding GHG weights to the index at £26.50/t CO2e SPC would enable there to be 54% greater reductions in GHG per breeding ewe relative to the current index. It would also result in 165 grams of GHG per kg lamb more progress in reducing GHG.

Table 4.7. Summary of crossing (upland) sheep annual selection response outcomes over the seven breeding objectives

Breeding objectives and overall responses pa.Selection index weights used

Units Current GHG1 GHG2 Eco+ CO2£a

Eco+ CO2£b

Eco+ CO2£c

Eco+ CO2£d

Current index £/ewe £0.72 £0.35 £0.35 £0.70 £0.67 £0.66 £0.54GHG reduction per ewe

kg CO2e/ewe

-6.64 -13.68 -13.58 -8.68 -10.29 -10.79 -12.87

GHG reduction per kg product produced

g CO2e/ kg product

-302.09 -620.27 -624.74 -394.12 -467.21 -489.91 -584.05

EU ETS 2009 carbon price (£12/t CO2e)

£/ewe 0.78 0.48 0.48 0.79 0.77 0.76 0.67

UK SPC 2009 (£26.50/t CO2e)

£/ewe 0.90 0.73 0.72 0.94 0.96 0.96 0.90

UK SPC 2020 (£32.90/t CO2e)

£/ewe 0.93 0.80 0.79 0.99 1.01 1.01 0.96

Worst case (£100/t CO2e)

£/ewe 1.37 1.69 1.67 1.55 1.68 1.67 1.80

4.2.3. Terminal sheep results

Table 4.8 shows the set of 7 selection index weights that were used in the selection index model for terminal sheep and Table 4.9 shows the expected annual responses in the traits in the selection index with each of the sets of index weights.

Terminal sheep had fewer profit traits compared to hill and crossing sheep as the current index was more focussed on direct growth and carcass composition traits on lambs rather than maternal traits (Table 4.9). However, the impact on correlated traits of selecting on the current index is shown in Appendix 7. The two lamb growth traits, (8WK-D and SWT) 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

46

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.

Table 4.8. Selection index weights used in terminal sheep including the current set of economic weights (Current), environmental weights expressed per breeding ewe (GHG1) and per kg of lamb meat (GHG2) and combined economic and environmental weights at four carbon prices (Eco+CO2£a-d) (Please refer to Table 3.3 for detailed descriptions of traits and units).

Breeding objectives and selection index weightsCurrent

(£)GHG1

(kg CO2e)

GHG2(kg

CO2e)

Eco+ CO2£a

(£)

Eco+ CO2£b

(£)

Eco+ CO2£c

(£)

Eco+ CO2£d

(£)Trait namesLean wt 2.66 0 0 2.66 2.66 2.66 2.66Fat wt -1.76 0 0 -1.76 -1.76 -1.76 -1.76RFI-Lambs 0 -0.44 -0.02 -0.01 -0.01 -0.01 -0.04Lamb Survival-Direct

0 -1.02 0.07 -0.010 -0.03 -0.03 -0.10

8 WK-Direct 0 0.45 0.02 0.01 0.01 0.01 0.05SWT 0 4.06 0.18 0.05 0.12 0.13 0.41

Table 4.9. Index and correlated trait responses for terminal sheep when seven different breeding objectives were selected (Please refer to Table 3.3 for detailed descriptions of traits and units).

Breeding objectives and trait responses (in trait units pa.)Current GHG1 GHG2 Eco+

CO2£aEco+

CO2£bEco+

CO2£cEco+

CO2£dTrait namesLean wt 0.156 0.044 0.047 0.158 0.160 0.161 0.167Fat wt -0.116 0.072 0.075 -0.114 -0.110 -0.108 -0.091RFI-Lambs -0.046 -0.011 -0.009 0.048 0.049 0.050 0.055Lamb Survival-Direct

0.001 0.000 0.001 0.001 0.001 0.001 0.001

8 WK-Direct 0.038 0.188 0.190 0.038 0.038 0.038 0.037SWT 0.011 0.210 0.210 0.024 0.040 0.046 0.106

Table 4.10. Summary of terminal sheep annual selection response outcomes over the seven breeding objectives

Breeding objectives and overall responses pa.Selection index weights used

Units Current GHG1 GHG2 Eco+ CO2£a

Eco+ CO2£b

Eco+ CO2£c

Eco+ CO2£d

Current index £/ewe £0.62 -£0.01 -£0.01 £0.62 £0.62 £0.62 £0.60GHG reduction per ewe

kg CO2e/ewe

-0.04 -0.94 -0.94 -0.09 -0.16 -0.18 -0.42

GHG reduction per kg product produced

g CO2e/ kg product

-1.89 -41.81 -41.89 -4.16 -6.95 -8.16 -18.82

EU ETS 2009 carbon price (£12/t CO2e)

£/ewe £0.62 £0.00 £0.01 £0.62 £0.62 £0.62 £0.61

UK SPC 2009 (£26.50/t CO2e)

£/ewe £0.62 £0.02 £0.02 £0.62 £0.62 £0.62 £0.62

UK SPC 2020 £/ewe £0.62 £0.02 £0.02 £0.62 £0.62 £0.62 £0.62

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(£32.90/t CO2e)Worst case (£100/t CO2e)

£/ewe £0.62 £0.09 £0.09 £0.63 £0.64 £0.64 £0.65

The current index weights were calculated to result in £0.62 per breeding ewe per annum overall progress (Table 4.10). 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.

When the cost of carbon was taken into account in the index weights there was no reduction in the economic overall responses until GHG costs were above £32.90/t CO2e.

4.2.4. Maternal beef results

Table 4.11 shows the set of 7 selection index weights that were used in the selection index model for maternal beef and Table 4.12 shows the expected annual responses in the traits in the selection index with each of the sets of index weights.

Table 4.11. Selection index weights used in maternal beef including the current set of economic weights (Current), environmental weights expressed per breeding cow (GHG1) and per kg of meat (GHG2) and combined economic and environmental weights at four carbon prices (Eco+CO2£a-d) (Please refer to Table 3.7 for detailed descriptions of traits and units).

Breeding objectives and selection index weightsCurrent

(£)GHG1

(kg CO2e)

GHG2(kg

CO2e)

Eco+ CO2£a

(£)

Eco+ CO2£b

(£)

Eco+ CO2£c

(£)

Eco+ CO2£d

(£)Trait namesWT200-M 0.73 5.75 0.05 0.80 0.88 0.92 1.31CW 0.70 8.34 0.07 0.80 0.92 0.97 1.53CCS 6.70 0 0 6.70 6.70 6.70 6.70GL-D -1.17 0 0 -1.17 -1.17 -1.17 -1.17CD-D -2.88 0 0 -2.88 -2.88 -2.88 -2.88CD-M -2.19 0 0 -2.19 -2.19 -2.19 -2.19CI -0.83 5.77 -0.01 -0.76 -0.68 -0.64 -0.25AFC -48.11 0.54 -0.002 -48.10 -48.10 -48.09 -48.10LS 6.63 0 0 6.63 6.63 6.63 6.63MW -0.23 -0.95 -0.01 -0.24 -0.26 -0.26 -0.33RFI-Grow 0 -0.26 -0.002 -0.003 -0.01 -0.01 -0.03RFI-Brd 0 -0.33 -0.003 -0.004 -0.01 -0.01 -0.03

Table 4.12 shows that selection on an index based on environmental weights resulted in a higher expected annual response in carcass weight (CW) compared to selection on the current index (2.7 kg/annum vs. 3.5 kg/annum respectively). However, the current index had greater response in reducing mature weights (MW) with an annual response of – 3.5 kg compared to – 0.45 kg for the environmental (GHG1) index.

The beneficial response in CW for the environmental index had an unfavourable correlated response on gestation length (GL-D) expected annual response of -0.05 days/annum with the current index compared to +0.022 days/annum with an

48

environmental index. The excepted response in calving interval (CI) also changed from favourable response of -0.7 days/annum with the current index to an unfavourable expected response +0.32 days/annum with an environmental index.

Table 4.12. Index and correlated trait responses for maternal beef when seven different breeding objectives were selected (Please refer to Table 3.7 for detailed descriptions of traits and units).

Breeding objectives and trait responses (in trait units pa.)Current GHG1 GHG2 Eco+

CO2£aEco+

CO2£bEco+

CO2£cEco+

CO2£dTrait namesWT200-maternal 0.168 0.009 0.000 0.157 0.146 0.141 0.103CW 2.699 3.548 3.684 2.855 3.001 3.054 3.361CFS 0.019 0.016 0.015 0.019 0.019 0.019 0.019CCS 0.031 0.018 0.018 0.030 0.030 0.030 0.027GL-direct -0.050 0.022 0.017 -0.044 -0.038 -0.036 -0.018CD-direct -0.006 -0.009 -0.003 -0.007 -0.007 -0.008 -0.009CD-maternal -0.013 -0.017 -0.017 -0.014 -0.014 -0.015 -0.016CI -0.711 0.317 -0.448 -0.632 -0.546 -0.512 -0.256AFC -0.005 -0.010 -0.005 -0.005 -0.006 -0.006 -0.008LS 0.037 0.012 0.015 0.035 0.034 0.033 0.027MW -3.505 0.449 -0.079 -3.221 -2.912 -2.787 -1.827RFI-growing -4.020 -9.931 -10.432 -4.700 -5.377 -5.634 -7.339RFI-breeding -4.092 -7.489 -7.386 -4.531 -4.960 -5.121 -6.152SF 0.001 -0.001 -0.001 0.001 0.000 0.000 0.000BSV-direct 0.000 0.000 0.000 0.000 0.000 0.000 0.000BSV-maternal 0.000 0.000 0.000 0.000 0.000 0.000 0.000WSV-direct 0.000 0.000 0.000 0.000 0.000 0.000 0.000WSV-maternal 0.000 0.000 0.000 0.000 0.000 0.000 0.000DS 0.010 0.010 0.011 0.011 0.011 0.011 0.011

An overall annual response of £4.21 per breeding cow was calculated for the current index for maternal beef (Table 4.13). The two environmental indices had a lower expected economic response with £2.86 and £3.44 for the GHG/breeding cow (GHG1) and GHG/kg beef indices (GHG2) respectively. The current index had a favourable impact on GHG emissions reducing them by 25 kg CO2e/cow/year. Selecting on the environmental index, GHG1, resulted in an expected annual reduction in GHG emissions of 36 kg CO2e/cow/year, equating to a cumulative annual reduction of 1.2% in GHG emissions over the base scenario.

The maternal beef selection index was the most complex selection index as it had the largest number of traits, current weights and environmental weights included. Therefore some of the correlated responses may be counter-intuitive. It should be noted, that with the wider rage of environmental weights could be calculated for the maternal beef situation. This was the only breeding scenario in sheep, beef and dairy that should a difference in responses between the 2 sets of environmental index weights. Also, the maternal beef breeding scenario showed the closest economic and environmental responses between the current index and the environmental index.

49

Table 4.13. Summary of maternal beef annual selection response outcomes over the seven breeding objectives

Breeding objectives and overall responses pa.Selection index weights used

Units Current GHG1 GHG2 Eco+ CO2£a

Eco+ CO2£b

Eco+ CO2£c

Eco+ CO2£d

Current index £/cow £4.21 £2.86 £3.44 £4.17 £4.13 £4.12 £3.88GHG reduction per cow

Kg CO2e/cow

-25.10 -36.09 -33.36 -26.85 -28.51 -29.12 -32.82

GHG reduction per kg product produced

g CO2e/kg product

-259.82 -283.45 -306.16 -269.27 -277.60 -280.467 -294.41

EU ETS 2009 carbon price (£12/t CO2e)

£/cow £4.50 £3.30 £3.84 £4.48 £4.47 £4.46 £4.27

UK SPC 2009 (£26.50/t CO2e)

£/cow £4.90 £3.85 £4.36 £4.91 £4.93 £4.92 £4.79

UK SPC 2020 (£32.90/t CO2e)

£/cow £5.02 £4.04 £4.53 £5.04 £5.06 £5.06 £4.95

Worst case (£100/t CO2e)

£/cow £6.73 £6.47 £6.78 £6.86 £6.99 £7.04 £7.17

4.2.5. Terminal beef results

Table 4.14 shows the set of 7 selection index weights that were used in the selection index model for terminal beef and Table 4.15 shows the expected annual responses in the traits in the selection index with each of the sets of index weights.

Table 4.14. Selection index weights used in terminal beef including the current set of economic weights (Current), environmental weights expressed per breeding cow (GHG1) and per kg of meat (GHG2) and combined economic and environmental weights at four carbon prices (Eco+CO2£a-d) (Please refer to Table 3.7 for detailed descriptions of traits and units).

Breeding objectives and selection index weightsCurrent

(£)GHG1

(kg CO2e)

GHG2(kg

CO2e)

Eco+ CO2£a

(£)

Eco+ CO2£b

(£)

Eco+ CO2£c

(£)

Eco+ CO2£d

(£)Trait namesCW 1.20 5.27 0.04 1.26 1.34 1.37 1.73CFS -6.00 0 0 -6.00 -6.00 -6.00 -6.00CCS 7.00 0 0 7.00 7.00 7.00 7.00GL-D -1.00 0 0 -1.00 -1.00 -1.00 -1.00CD-D -2.88 0 0 -1.00 -2.88 -2.88 -2.88RFI-Grow 0 -0.16 -0.002 -0.002 -0.004 -0.01 -0.02WSV-D 0 -10.82 0.05 -0.13 -0.29 -0.36 -1.08

As with terminal sheep the current weights and breeding scenario focus on a limited number of production related traits. 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 (GL-direct) and calving difficulty (CD-direct), 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 4.15. Index and correlated trait responses for terminal beef when seven different breeding objectives were selected (Please refer to Table 3.7 for detailed descriptions of traits and units).

Breeding objectives and trait responses (in trait units pa.)Current GHG1 GHG2 Eco+

CO2£aEco+

CO2£bEco+

CO2£cEco+

CO2£dTrait namesCW 2.317 2.514 2.504 2.343 2.370 2.380 2.454CFS -0.035 0.047 0.048 -0.031 -0.027 -0.025 -0.011CCS 0.063 0.033 0.032 0.062 0.060 0.060 0.055GL-direct 0.016 0.068 0.066 0.019 0.022 0.023 0.033CD-direct 0.005 0.012 0.012 0.005 0.006 0.006 0.007RFI-growing -2.721 -8.743 -9.006 -3.039 -3.386 -3.528 -4.685SF -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001BSV-direct 0.000 0.000 0.000 0.000 0.000 0.000 0.000WSV-direct 0.000 0.000 0.000 0.000 0.000 0.000 0.000DS 0.009 0.006 0.006 0.009 0.009 0.009 0.009

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.

When the cost of carbon was taken into account in the current farm profit indexes (in the Eco+ CO2£a indexes) the cost of carbon did not significantly reduce the overall economic response until carbon prices were over £32.90. Even at a carbon price of £100/t CO2e, the economic responses was only £0.06/ breeding cow less per annum.

Table 4.16. Summary of terminal beef annual selection response outcomes over the seven breeding objectives

Breeding objectives and overall responses pa.Selection index weights used

Units Current GHG1 GHG2 Eco+ CO2£a

Eco+ CO2£b

Eco+ CO2£c

Eco+ CO2£d

Current index £/cow £3.40 £2.87 £2.84 £3.40 £3.39 £3.39 £3.34GHG reduction per cow

kg CO2e/cow

-12.65 -14.65 -14.64 -12.84 -13.03 -13.11 -13.68

GHG reduction per kg product produced

g CO2e/kg product

-98.12 -118.03 -118.17 -99.81 -101.57 -102.27 -107.53

EU ETS 2009 carbon price (£12/t CO2e)

£/cow £3.55 £3.06 £3.03 £3.55 £3.55 £3.55 £3.51

UK SPC 2009 (£26.50/t CO2e)

£/cow £3.74 £3.25 £3.23 £3.74 £3.74 £3.74 £3.71

UK SPC 2020 (£32.90/t CO2e)

£/cow £3.82 £3.38 £3.35 £3.83 £3.83 £3.83 £3.81

Worst case (£100/t CO2e)

£/cow £4.68 £4.37 £4.35 £4.70 £4.72 £4.72 £4.74

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4.2.6. Dairy cattle results

Table 4.17 shows the set of 7 selection index weights that were used in the selection index model for dairy and Table 4.18 shows the expected annual responses in the traits in the selection index with each of the sets of index weights.

Table 4.17. Selection index weights used in dairy including the current set of economic weights (Current), environmental weights expressed per breeding ewe (GHG1) and per kg of lamb meat (GHG2) and combined economic and environmental weights at four carbon prices ranging from £12/t CO2e to £100/t CO2 e (Eco+CO2£a-d)

Breeding objectives and selection index weightsCurrent

(£)GHG1

(kg CO2e)

GHG2(g

CO2e)

Eco+ CO2£a

(£)

Eco+ CO2£b

(£)

Eco+ CO2£c

(£)

Eco+ CO2£d

(£)Trait namesMILK -0.027 0.681 0.079 -0.019 -0.009 -0.005 0.041FAT 0.800 -6.166 -0.719 0.726 0.637 0.597 0.183PROT 1.710 0 0 1.710 1.710 1.710 1.710LS 25.400 68.908 7.960 26.227 27.226 27.667 32.291SCC -0.190 0 0 -0.190 -0.190 -0.190 -0.190MAM 1.810 0 0 1.810 1.810 1.810 1.810LOCO 1.130 0 0 1.130 1.130 1.130 1.130CI -0.350 0 0 -0.350 -0.350 -0.350 -0.350NR56 2.160 3.383 0.615 2.201 2.250 2.271 2.498

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). 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.

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 4.19). 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.

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Table 4.18. Index and correlated trait responses for dairy cattle when seven different breeding objectives were selected

Breeding objectives and trait responses (in trait units pa.)Current GHG1 GHG2 Eco+

CO2£aEco+

CO2£bEco+

CO2£cEco+

CO2£dTrait namesMILK 79.29 116.05 116.06 87.58 95.44 98.36 114.83FAT 3.94 2.58 2.58 4.01 4.05 4.05 3.91PROT 2.96 2.70 2.70 3.09 3.19 3.23 3.34LIFESPAN 0.055 0.014 0.014 0.053 0.051 0.050 0.041MAST 0.0015 0.0039 0.0039 0.0018 0.0022 0.0023 0.0030LAME 0.0006 0.0001 0.0001 0.0006 0.0006 0.0006 0.0004CALVING INT 0.37 0.79 0.79 0.41 0.45 0.46 0.55NR56 -0.0027 -0.0047 -0.0047 -0.0029 -0.0031 -0.0032 -0.0037CS* -0.021 -0.033 -0.033 -0.024 -0.026 -0.027 -0.032SCC -0.0045 -0.0005 -0.0005 -0.0046 -0.0046 -0.0046 -0.0044MAM 0.0150 0.0079 0.0078 0.0150 0.0140 0.0140 0.0110LOCO 0.0083 0.0046 0.0046 0.0082 0.0081 0.0080 0.0070* CS = Condition score, 1 = thin, 9 = fat

Table 4.19. Summary of dairy annual selection response outcomes over the seven breeding objectives

Breeding objectives and overall responses pa.Selection index weights used

Units Current GHG1 GHG2 Eco+ CO2£a

Eco+ CO2£b

Eco+ CO2£c

Eco+ CO2£d

Current index

£/cow£7.11 £3.21 £3.21 £7.07 £6.98 £6.92 £6.24

GHG reduction per cow

kg CO2e/cow

-33.50 -64.07 -64.08 -38.56 -43.55 -45.43 -56.87GHG reduction per kg product produced

g CO2e/kg product

-14.15 -28.79 -28.79 -16.47 -18.76 -19.64 -24.98EU ETS 2009 carbon price (£12/t CO2e)

£/cow

£3.90 £7.47 £7.47 £4.49 £5.07 £5.29 £6.63UK SPC 2009 (£26.50/t CO2e)

£/cow

£7.51 £3.98 £3.98 £7.53 £7.50 £7.47 £6.92UK SPC 2020 (£32.90/t CO2e)

£/cow

£7.99 £4.91 £4.91 £8.09 £8.13 £8.13 £7.75Worst case (£100/t CO2e)

£/cow£8.21 £5.32 £5.32 £8.34 £8.41 £8.42 £8.11

4.3. Discussion

4.3.1. Sheep

Three different sheep breeding scenarios were examined in this project, namely hill sheep system (e.g., Scottish Blackface), crossing upland sheep system (e.g., Mules) and terminal lowland sheep system (e.g., Suffolks). This represents the stratified nature of sheep production and breeding in the UK. Each of these systems will have different “drivers” to overall system efficiency and therefore traits of importance in the

53

breeding goal. This is reflected in the current range of traits considered in the selection index for each of the systems.

In both terminal and crossing sheep systems the current selection index based on economic performance was predicted to have a favourable expected response not only in overall profitability but also in terms of reduction in overall GHG emissions. However, in hill sheep the current index was predicted to have a negative impact on overall GHG emissions. The selection index in hill sheep sector currently places a large emphasis on lamb survival and maternal performance (MAT) of the ewes and therefore there is a favourable expected response in these traits when selecting on the current index. However, improvements in maternal performance and lamb survival will result in more lambs in a given system for a fixed number of ewes, requiring feeding and producing associated GHG emissions. This converse relationship between the current index based on economic performance and GHG emissions was only seen in the hill sheep scenario.

Selection based on an environmental index was predicted to have a lower overall economic response in all sheep systems, with a predicted negative economic response in hill sheep systems. This suggests that selecting on an environmental index in sheep systems will be conflict with the overall economic performance as reflected in the current economic index. The conflict between expected economic and environmental responses when selection changes from the economic index to an environmental index in the hill sheep is not seen in any other of the species and index types studied. This highlights that sometimes selection for economic efficiency within a farm gate may not always equal environmental within that same farm gate. It should also be noted that both the economic and the environmental models allowed for additional feed to be imported to the system to meet the dietary requirements of animals, with the respective economic and environmental cost incorporated. In the case of the environmental index this may allow the weight of “production” (i.e., feed and growth) efficiency to inflate to a level significantly higher than the economic index would suggest. A future step may consider looking at both the economic and environmental models in a resource limited scenario (i.e., when external feed sources are not available to bring on farm) and the models must optimise their economic and environmental inputs/outputs based on resources available within the farm gate only.

Figure 4.2 shows the changes in the relative emphasis of traits when changing the emphasis from the economic scenario to one that would focus on the environmental impact of the system. This figure shows that under the economic assumptions of hill sheep production given by Conington et al. (2004) that maternal and young lamb trait performance was particularly important to the economics of the overall system with a relative emphasis in the breeding goal of 54%. This is compared to a relative emphasis of 10% for maternal traits in the environmental index scenario. This highlights the relative importance of maternal characteristics in the hill scenario, both in terms of the more extensive nature of the production system but also in the role of the hill sector in producing ewes of good maternal characteristics. This is a reflection of the role that the hill breeds have in the UK sheep industry. Sheep breeds in the UK can be “generally” classified in relation to their role in the sheep industry, e.g. hill, crossing and terminal breed types. Within this, crossing breeds are typically mated to hill breeds to generate crossbred breeding ewes, which do not generate their own replacements (Amer et al., 2007). Therefore, the hill breeds have a duel role to play - both within their own production system but also in terms of providing replacement females (i.e., good mothers) to the crossing sector of the sheep industry.

54

Figure 4.2 also shows that the economic index scenario in hill sheep considered a different set of traits than were considered in the environmental index. For example, the economic index has an index weight on carcass fat and muscle for which farmers are paid differentially on. However, it is currently impossible to examine the environmental impact of alternative carcass classifications, above and beyond age at reaching target carcass weight and conformation, as less optimal carcasses would not necessarily be rejected from the production system. Also, the environmental index scenario includes traits related to the feed efficiency in the ewes and lambs, which are not included in the economic index. The RFI traits modelled are not routinely available in the sheep industry and therefore are more horizon scanning traits. However, even if RFI traits were removed from the environmental index the relative emphasis of the maternal traits in the index would only increase to approximately 18% of the overall breeding goal.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

EconInd (per ewe) EnviInd (per ewe) EnviInd (per kg)

Index

Rela

tive

Emph

asis

Carcass Fat+Muscle Mature Size Maternal traits RFI Ewe longevity+lamb survival

Figure 4.2. Relative emphasis of carcass (fat and muscle), mature size, maternal traits (maternal output, litter size reared and 8-week weight), residual feed intake traits and survival (ewe and lamb) in the alternative (economic and environmental) hill sheep index scenarios.

The unit of expression for the environmental index (per ewe vs. per kg of lamb carcass) had little impact on the expected overall economic and environmental responses. However, there where small differences in the expected responses of some of the component traits of the index, particularly ewe longevity and lamb survival.

Both economic and environmental indices had both favourable and unfavourable expected responses in traits relating to the functionality (or fitness) of the animal (i.e., welfare style traits). In all sheep scenarios the expected response in ewe longevity was predicted to improve when selecting on an environmental index (per kg of lamb carcass, GHG2) compared to the current index. The largest difference was seen in the hill sheep scenario where ewe longevity was predicted to improve by 0.003 years/annum compared to 0.043 years/annum when selecting on an environmental index. There were contrasting responses in health traits between the current and the environmental index and between the different sheep scenarios. In the terminal

55

sheep scenario the expected responses in faecal egg counts (FECN and FECS, Appendix 7) are expected to improve when selecting on an environmental index compared to the current index. The expected responses in the fitness traits in sheep were complex and highlight that there is an element of trade-off between overall economic, environmental and welfare performance.

In all sheep scenarios the correlated trait of residual feed intake (RFI) was included in the selection index tool. In the current index there is no weight on RFI as the trait is not recorded and there is little work done on it in UK circumstances. However, from overseas studies it is known that there is a genetic component to the trait and that it is correlated to many of the traits in the index. Improving RFI would have a favourable impact of the overall GHG emissions from ruminant systems as less feed is required to meet a given level of production output. Because of this relationship, an environmental index weight was calculated for added to the environmental index, utilising information from overseas. The addition of RFI as an index trait in the environmental index was shown to have a favourable impact on the trait in all scenarios, improving the response in the trait compared to the current index and therefore improving the expected response in terms of the reduction of GHG emissions. As the trait is currently not recorded in the UK we currently would have no way adding such a trait to an index and therefore the responses described my not be realised. However, when the trait is removed from the environmental index in the hill sheep scenario there was very little impact on the overall economic and environmental responses.

The stratified nature of the UK sheep industry (e.g., hill bred ewes acting as female replacements in the crossing sector) may mean that considering environmental impact within a production system farm gate may proportion emissions unfairly to that system. For example, in the economic scenario in the hill sheep the maternal traits make up a significant proportion of the breeding goal. This is of benefit to the crossing sector of the UK sheep industry, who receives higher quality replacement females from the hill sector but without the environmental cost of incorporating a heavier weighting on maternal characteristics. The economic benefit to the hill system can be reflected in terms of the increased value of replacement females from that sector. However, it is harder to disentangle the emissions associated with the production of better females from the carbon footprint of that hill system. The farm-gate can be viewed as a sensible boundary when considering agricultural GHG emissions as farmer actions to reduce GHG emissions will generally be based on an on-farm measure being adopted and impact measured/estimated within that farm gate. However, when different farming types within an industry, such as sheep, interact and are linked it may prove difficult to account for who is responsible for what emissions, and transact the carbon costs in similar manner to the cash costs in an economic framework. This is particularly relevant when production systems are integrated, in a conscious/planned or unconscious/unplanned manner, as the value of the emissions of the final end product (e.g., leg of lamb) involves many steps on the production chain, potentially over a number of years. It is also complicated by the fact that there are numerous individual decision makers acting on a range of drivers, specific to each system. In the case of GHG emissions it may be necessary to work towards an integrated breeding goal, rather than a within breed and/or within system breeding goal, for those systems that interact (input/output) with other production systems. This will require more complex whole production chain and farming systems modelling as well as farmers/breeders supporting and utilising a joint overall breeding goal for a benefit that may not be easily identified on individual farms.

4.3.2. Beef

56

Two different beef breeding scenarios were examined in this project, namely maternal beef system and a terminal beef system. This represents 2 distinct breeding policies within beef production in the UK with certain breeds tending towards breeding animals for their maternal characteristics and others focussing on breeding bulls to produce valuable slaughter progeny. The current beef maternal index contains a total of 10 traits (Table 4.11) focussing on cow fertility and maternal performance whereas the terminal beef index contains 5 traits Table 4.14) focussing on carcass performance traits.

All of the indices (current and environmental) studied showed a favourable overall economic and environmental response. This suggests that the selection indices in beef breeding are expected to reduce the overall GHG emissions of beef systems, both maternal and terminal, as well as improve overall economic performance. Additional benefits in GHG emissions could be achieved if focus of selection shifted to an environmental rather than economic goal, improve the rate of reduction in GHG emissions by 44% in maternal beef breeding systems and 16% in terminal beef breeding systems compared to reduction seen with using current index weights.

There is a wider understanding in residual feed intake (RFI) in beef in comparison to sheep. As with sheep, RFI is currently not recorded routinely in the UK and not included in the selection index for beef. This study also examined the impact of adding it as trait to environmental beef selection indices using information from literature due to it potential as a target trait for reduction in GHG emissions. The current index in both maternal and terminal beef was predicted to have a favourable impact on RFI such that it was predicted to decrease, meaning that less feed is required to meet a given level of output. The expected response in RFI was predicted to improve when selection is based on an environmental index in both maternal and terminal beef scenario.

Table 4.20 shows that expected responses for environmental indices that do not include residual feed intake and shows that over 97% of the reduction in GHG emissions can be achieved with an environmental index without RFI included in comparison to one with it. The expected response in RFI is reduced when it is dropped from the environmental index, dropping to 67% of the response when it is included in the environmental index. Although RFI has a favourable impact on the reduction of GHG emissions from ruminant systems it is only a component of the system that impacts on overall system emissions. It should also be noted that the overall economic response, assuming no economic impact of improving RFI, is also more favourable based on an environmental index without RFI than one with it incorporated. It is likely that improving RFI will have a favourable impact on economic performance of the system, due to reduced food costs. However, that is not incorporated in the current breeding goal and is therefore not included in this study. However, having index weights that do not include the RFI, when it is not yet available as a routine trait, is valuable as it means that there are index weights that could be used today with currently available genetic improvement tools.

57

Table 4.20. Index and correlated trait responses for maternal beef when seven different breeding objectives were selected (Please refer to Table 3.7 for detailed descriptions of traits and units).

Breeding objectives and trait responses (in trait units pa.)Current GHG1 GHG2 GHG1

(no RFI)GHG2

(no RFI)Trait names200d weight (kg) 0.168 0.009 0.000 0.100 0.102Carcass Wt (kg) 2.699 3.548 3.684 3.471 3.614Carcass Fat Score 0.019 0.016 0.015 0.022 0.022Carcass Conformation Score 0.031 0.018 0.018 0.025 0.025Gestation length (days) -0.050 0.022 0.017 0.029 0.024Calving Difficultly -0.006 -0.009 -0.003 -0.012 -0.005Calving Interval -0.711 0.317 -0.448 0.355 -0.519Lifespan 0.037 0.012 0.015 0.014 0.018RFI-growing -4.020 -9.931 -10.432 -6.593 -6.688RFI-breeding -4.092 -7.489 -7.386 -4.830 -4.321

Overall economic response (£/cow/yr) £4.21 £2.86 £3.44 £2.94 £3.61Overall GHG reduction per cow (kg CO2e/cow/yr)

-25.10 -36.09 -33.36 -35.18 -31.88

GHG reduction per kg product produced (g CO2e/ kg product/ yr)

-259.82 -283.45 -306.16 -275.34 -299.97

As with sheep, the responses in the fitness traits in beef cattle with selection on the current or environmental index have some unfavourable and some favourable responses. Overall, there was little impact of young animal survival (BSV and WSV) when selecting on the current or environmental index in either maternal and terminal beef scenarios. In the terminal beef scenario, the increased weighting on production traits with the environmental index weights saw a predicted worsening of both gestation length (GL) and calving difficulty (CD) when compared to the current index.

4.3.3. Dairy

The recently published UK Milk Roadmap set targets for the dairy industry to reduced greenhouse gas emissions by 20-30% by the year 2020 (Dairy Supply Chain Forum, 2008). Therefore there are good reasons for producers to consider their emissions and to determine the relative cost-effectiveness of mitigation options to help the industry reach these targets in the easiest and potential most beneficial manner.

One dairy breeding selection scenario was explored as part of this study, reflecting the practice of within breeding selection in dairy cattle, not only within the UK but worldwide. Both selection on the current index and the environmental index was predicted to have a favourable economic and environmental response. Overall, the environmental index had double the expected beneficial response in GHG emissions but half the overall economic benefit. This highlights the potential conflict between economic and environmental goals in dairy systems.

Traits related to feed efficiency, such as RFI, were not included in the dairy cattle index weightings as the focus is on performance in mature animals (milking cows) as opposed to the growing animals followers). However, condition score (CS, Table 4.18) is strongly correlated to the feed intake relative to milk output of a dairy cow during lactation. Both the current and the environmental indices are predicted to have a unfavourable impact on CS, with a predicted negative response in CS -0.02 units selecting on the current index and -0.03 selecting on the environmental index. This

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indicates that selecting cows on the environmental index will result in cows getting thinner at a faster rate than the current index as cows are utilising their own body reserves to meet their lactation demands.

The methods used in this study to value the impact on GHG emissions of improvements in underlying biological traits is based on complex modelling of all GHG emissions in the farming system, including those related to the feed that livestock require. The wider up and down stream emissions, outside the farm gate, are not intrinsically included as it is expected that issues out with the farm gate (e.g., transportation, disposal from the home, processing) are constant per cow or per kg product. Where the boundary is placed when accounting fro GHG emissions can have an impact on the estimated impact of improving traits on overall system GHG emissions. For example, if the environmental index for dairy cattle only accounted for the methane emissions from the cows and followers as a result of enteric fermentation (i.e., no N2O emissions, emissions related to feed production or emissions related to manure management) than the index is predicted to have a higher weight on milk production compared to lifespan than an environmental index that includes all sources of GHG emissions (Table 4.21). This methane only index was predicted to have the lowest overall economic response of all indices studied. Although the overall environmental response, including all GHG emissions, was better than that of the current index the methane only index resulted in a lower GHG emissions response compared to the environmental index including all GHG emissions. This highlights the need to account for all GHG emissions when describing a ruminant livestock system and the impact of different GHG reduction mechanisms on overall system emissions. Focussing on one source of emissions may result in a mixed message and thus reduce the overall efficacy of GHG reduction methods.

Figure 4.3 shows how the index weights vary in dairy cattle as we move from the current index to an environmental index (GHG1 and GHG2) and to a combined index (Eco_a to Eco_d). This shows that the current index in dairy cattle has a relative weighting of 60% on traits related to production and 40% on fitness traits. Switching emphasis to an environmental index is expected to increase the overall emphasis of production traits (87%) in the breeding goal, even though improved longevity and fertility are predicted to have a favourable impact of GHG emissions. This highlights the importance of production efficiency relative to system efficiency in terms of overall system GHG emissions. Alternative index weights based on graduated scale of notional carbon prices combined with current economic weights are included it can be seen that the relative weight on production traits decreases to 52% for a carbon price of £32.90. However, as the price rises to £100 is can be seen that the relative weight shifts towards production traits again. This highlights the potential trade-offs between production and functional traits that may come about if the pressure within dairy systems are placed solely on environmental targets.

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Table 4.21. Summary of dairy annual selection response outcomes based on current index, environmental index containing all GHG emissions (GHG1) and an environmental index including methane due to enteric fermentation in milking and following herd (CH4only) over the seven breeding objectives

Breeding objectives and overall responses pa.

Selection index weights used Units Current GHG1 CH4onlyCurrent index £/cow £7.11 £3.21 £2.98GHG reduction per cow kg CO2e/cow -33.50 -64.07 -63.94GHG reduction per kg product produced

g CO2e/kg product -14.15 -28.79 -28.80

EU ETS 2009 carbon price (£12/t CO2e)

£/cow£3.90 £7.47 £7.45

UK SPC 2009 (£26.50/t CO2e) £/cow £7.51 £3.98 £3.75UK SPC 2020 (£32.90/t CO2e) £/cow £7.99 £4.91 £4.68Worst case (£100/t CO2e) £/cow £8.21 £5.32 35.09

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Current GHG1 GHG2 Eco_a Eco_b Eco_c Eco_d

Dairy Index

Rela

tive

Wei

ght

Production Longevity Health&Fertility

Figure 4.3. Relative emphasis of production, longevity and health and fertility traits in the 7 index weightings scenarios studied.

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5. Monogastrics

5.1. Background

In contrast to beef and sheep, breeding programmes for monogastric species (poultry and pigs) tend to be conducted within commercial companies and information on genetic variances and correlation estimates between different traits and index weights tend to be kept confidential. Therefore a detailed investigation of the effects of changes in index weight was more difficult for these species.

In a previous Defra-funded study (AC0204) the challenge of limited available data was overcome by focusing on industry relevant genetic trends that could more easily be obtained. The main focus of that study was to estimate the overall effect of genetic improvement across the whole of the UK industry in terms of emissions per tonne of product and the relative contribution of improvement in each trait to the overall change. The study was able to show that substantial reductions in emission (around 1% a year) had been achieved in both the pig and poultry industry over the 20 years prior to the study through the genetic improvement. Their results also suggested that this rate of reduction could also be maintained, if not increased, over the following 15 years.

The results presented in the AC0204 study were the average reductions expected across the range of production systems that was typical for the UK industry. The likely differences between different types of production systems were not considered. It was also assumed that genetic improvement in pigs was achieved though dissemination through the sale of high merit boars only, an assumption that has since been the subject of some debate. A number of other questions were also raised when the results for pigs were discussed with industry representatives, for example a) were there any other traits that could have an important impact on GHG emissions, b) could increasing average slaughter weights have an impact and c) what effect would feeding lower quality diets have on overall emissions. In view of the limited information on industry relevant genetic parameters and index weight, it was chosen to include these issues in the focus for the study.

The objectives of this study were to investigate:1) The effect of also assuming that improved genetics in the UK pig industry was

disseminated directly through the purchase of high genetic merit females as well as boars

2) If the same reduction in emissions and contribution from different traits was achieved across the range of production systems typically used in the UK of pigs and broilers.

3) If increasing sow longevity/productive life would affect overall emissions effect the overall level of emissions

4) If increasing average slaughter weights for pigs by 10kg would affect the overall level of emissions

5) If using lower quality diets would affect the overall level of emissions

5.2. Methods

All investigations were focused on using the Life Cycle Analysis (LCA) model developed at Cranfield University (as part of the Defra funded project, codes IS0205 and IS0222) to model the effects of changes in the performance of animals in specific livestock systems. In using the Cranfield LCA model the boundary for the systems considered was the farm gate. The version used in this study was version 50 which includes the IPCC 2006 emission factors.

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The scope of this report was limited to quantifying the Global warming potential of GHG emissions over 100 years (GWP). The GWP is expressed in CO2 equivalents and is mainly driven by emission of methane and nitrous oxide. All results were expressed as emissions per tonne of carcass weight.

5.2.1. Approach

The approach used was based on first assessing the average level of emissions given typical UK performance values in 2007 when animals were reared in each of the management system considered. Emission levels were then recalculated given the predicted performance for animals likely to be available 15 years in the future (2022). Predictions of the likely performance in 2022 were based on 2007 performance estimates and industry relevant estimates of linear genetic trends that were available or derived for each species and specific breeds within each species. The genetic trends available for pigs included: number born alive, daily gain and feed conversion efficiency (FCR). Genetic trends available for broilers included: eggs laid per breeder hen, days to finish, FCR, mortality and killing out proportion (KO%). All estimates of typical performance levels for different systems and genetic trends used were based on values derived as part of the AC0204 study.

5.2.2. Modulation

The effect of improved genetics is unlikely to be expressed to the same extent in all commercial production systems e.g. indoor versus free range systems for poultry. Whilst this is accepted, the level of typical modulation in different systems is difficult to determine. The issue was addressed in this study by using a scaling approach. Once the cumulative level of change due to genetic improvement was calculated, it was then expressed as a proportion of the current performance in one (generally the most common) scenario. This proportional change was then used to calculate the expected performance under each scenario for that species.

Increased rate of dissemination in pigsAs part of the AC0204 study it was assumed that improved genetics were disseminated to the UK industry through the sale of high merit boars. As part of this study, genetic trends were re-calculated assuming dissemination was also being achieved through the sale of high merit females.

Other traits of interestOptions to investigate changes in other animal variables within the available version of the LCA were relatively limited, however it was possible to investigate changes in the productive life of sows. Under the original assumptions for typical performance levels in 2007, the average productive life for sows managed in indoor and outdoor production systems was 2.34 and 2.65 years respectively. To investigate the effect of changes these values were increased by 50% (to 3.51 and 3.96 respectively) and the average emissions per unit product re-calculated.

Increasing carcass weightsIn the original study it was assumed that pigs were slaughtered at one of three weights, light (76 kg), medium (87Kg) and heavy (109kg), with the proportion of pigs slaughtered at each weight being 70, 20 and 10% respectively for indoor and outdoor systems and 0, 75 and 25 % respectively for organic systems.

Given the significant genetic improvement in growth rates that has been achieved over recent years, it should allow heavier slaughter weights to be achieved without

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increasing the average age at slaughter. To investigate the effect, end weights at each slaughter pint were increased by 10kg to 86, 97 and 119kg respectively.

Feeding poor quality dietsGiven the increased concerns over potential conflicts between growing cereals for human food and animal feed, particularly for monogastrics, there is growing interest in the potential of feeding lower quality diets. The actual composition of potential lower quality diets of interest was difficult to determine. As part of this study it was chosen to investigate the effect of replacing soya meal (a commonly used source of protein in current diets that is typically imported) with alternative home grown sources namely feed beans plus by-products rape meal and including small amounts of brewers grains and beet pulp in the diets for pregnant/lactating sows, rearing and finishing pigs. In each case the diets were formulated so as to match the same energy, protein and amino acid composition as the original diets. Once formulated, the likely food conversion ratio that could be expected when using the new diet was calculated using a pig growth model (Green & Whittemore, 2003 & 2005) and accounted for in the LCA model.

5.3. Results and Discussion

By assuming that improved genetics were disseminated through purchased high merit females as well as boars, the estimated genetic trends per year across the UK industry increased from 6.4 to 8.4 g for lifetime daily gain, from -0.020 to -0.024 for FCR and from 0.12 to 0.16 for Numbers born alive. As a result of these changes, the predicted reduction per tonne of product across the UK industry through genetic improvement was increased from 10.7 % to 14.4% (Table 5.1). The overall benefit was similar across production systems, with predictions tending to be highest for organic and lower for outdoor systems, but differences were less than 1%. The contribution from selection on different traits was also similar across production systems. The only exception was daily live weight gain in organic systems. For both indoor and outdoor systems pigs are finished indoor and a higher growth rate to fixed slaughter weights results in a saving in electricity (heat and light). For the organic systems it was assumed that all pigs were finished outdoor and thus this additional benefit was not seen, and resulted in a higher impact for an improvement in FCR.

Table 5.1. Predicted GWP reductions for different pig production system through genetic improvement over 15 year time horizon and the relative contribution from changes in different traits

Individual system typeWhole UK industry†

Indoor Outdoor Organic

Total reduction (% per t carcass wt) 14.4 14.6 14.0 14.7

Trait contribution (% of total)

No. born alive 27 29 23 32Daily gain 28 28 29 15FCR 45 43 48 53

As for pigs, the differences between production systems for broilers were relatively small, but predicted reduction tended to be higher for organic compared to other systems types (Table 5.2).

By increasing the productive life of sows overall emissions per unit product decreased (Table 5.3). However, despite the substantial increase investigated the benefit was relatively small, being less than 1%. When slaughter weights were

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increased the emission per unit product also increased, mainly as a result of an expected reduction in FCR with the increase in weight/size.

Table 5.2. Predicted GWP reductions for different broiler production system through genetic improvement over 15 year time horizon and the relative contribution from changes in different traits

Individual system typeWhole UK industry†

Housed Free Range

Organic

Total reduction (per tonne) 17.2 17.2 17.0 18.2

Trait contribution(% of total)

Eggs Laid 4 4 3 2Days finishing 25 25 21 25FCR 48 48 52 50Mortality 8 8 8 7KO% 16 16 16 16

Table 5.3. Effects of different changes in management/genetics on GWP of GHG emissions per unit product across the typical range of UK pig systemsChange investigated Effect on emission per tonne carcass weight+50% increase in sow productive life <-1%+10kg average slaughter weights +1%Feeding alternative diets† -4%†Includes the predicted 5% reduction in FCR

Larger effects have been seen in ruminant species particularly for productive life/longevity (see results from AC0204 for example). These results are likely to reflect the relatively low overhead cost of the breeding herd for pigs in terms of overall emissions when compared to ruminant species, as a result of the substantially higher levels of fecundity.

The alternative diets formulated were calculated to result in a 5% lower feed conversion ratio. Despite this reduction in FCR, the global warming potential of the GHG emissions per tonne of carcass weight were predicted to decrease by 4%. This reduction was driven by the use of by-products and a reduction in the emissions of nitrous oxide, as actual emissions of methane increased. Gaseous emissions of ammonia also increased, as a result of the fertiliser applied to grow the rape seed crops. Note that ruminants compete for the use of by-products and are generally better suited to them.

This study was not intended to be a comprehensive study of the options and effects of using alternative lower quality diets, as it was not possible as part of a small study such as this to investigate the multitude of issues that need to be taken into account. However it does help to highlight some of the complexities that need to be considered. In this case, despite a lower efficiency of conversion by the animal, the GWP of average emissions per tonne of product decreased by using the alternative diets but that effect was not true for all GHG gases or ammonia.

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6. Market forces/incentives required to utilise breeding tools that reduce GHG emissions

Jones et al. (2008) showed that historic selection on production traits (e.g. milk yield, fertility, growth efficiency) in UK livestock species has resulted in an average 1% per year reduction in GHG production per unit food produced. The reduction was shown to be greatest in those species with more widespread use of genetic improvement such as layer hens, broiler chickens, pigs and dairy cattle. However, the reductions were a great deal smaller in beef cattle and sheep. This was due to poorer rates of genetic improvement across the population in these sectors and poor dissemination of information from elite breeders to the commercial populations. However, within the elite breeding population Jones et al (2008) estimated that historic genetic improvement has likely also resulted in a beneficial impact on GHG emissions and that this has not had the impact in the wider population due to poor uptake of elite breeding stock. Improving the dissemination of elite genetics into the wider commercial beef herds and sheep flocks is an important step to not only improve the economic performance of commercial systems but also realising the environmental benefit of historic (Jones et al, 2008) and/or future alternative indices (this study).

Nielsen et al. (2008) reviewed methods that could be used to include environmental and welfare considerations in breeding goals. Restricted or desired gains approaches derive index weights that restrict unwanted changed or achieve the desired response in traits of interest. For example, Wall et al. (2007) showed how restricted index methodology could be used to halt the expected genetic decline in fitness traits in dairy cattle if selection were to continue on the current UK economic index. The difficulty in restricted/desired gains index methodology is developing a robust way of deciding on the desired outcomes of selection.

Table 6.1 shows that how restricted index methodology could be used to help quantify the potential trade-off between selection on an index based on profit (current index), environment (GHG1) and livestock welfare (restricted). The latter of these indices was based on responses from an index that would halt the decline in the health and fertility traits as seen with the current index. All three indices were predicted to result in a overall favourable economic response, with the current index resulting in the highest overall economic response per cow (£7.11), the welfare index the second highest (£6.80) and the environmental index the lowest (£3.21). All 3 indices were also predicted to have a favourable outcome on predicted GHG emissions with the environmental index the highest (-64.1 kg CO2e/cow), the current index the second highest (-33.5 kg CO2e/cow) and the welfare index the lowest (-20.5 kg CO2e/cow). In terms of health and welfare of the cows (as defined by improving longevity, health and fertility traits) the welfare index was predicted to have the highest welfare benefit of all three, the current index the second highest (e.g., increase in the cases of mastitis by 0.0015 case/cow/year) and the environmental the lowest (e.g., increase in the cases of mastitis by 0.0039 case/cow/year).

Results such as those in Table 6.1 can help inform producers, retailers and/or policy makers on the trade-offs within the livestock system that may occur in a number of key outputs such as sustainability, animal welfare and environmental impact. This may help to put a value on the premium that would need to flow to producers to achieve a number of goals from their farming system. For example, if a dairy producer were to focus on producing high welfare milk and would choose bulls based on such an index than this would cost £0.31/cow/year and result in a slower reduction in GHG emissions 13 kg CO2e/cow/year compared to current index. It should be noted that these costs would cumulate year on year and would be worth

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£0.72/cow and 26 kg CO2e/cow/year in two years relative to progress with the current index. These differences in economic and environmental margins would potentially need to be accounted for in the overall price that a producer is paid for milk.

Table 6.1. Summary of dairy annual selection response outcomes based on current index, environmental index containing all GHG emissions (GHG1) and a restricted index where expected responses health and fertility traits are held at zero.

Breeding objectives and trait responses (in trait units per cow per annum)

Current GHG1 RestrictedTrait namesMILK 79.29 116.05 51.25FAT 3.94 2.58 3.08PROT 2.96 2.70 2.35LIFESPAN 0.055 0.014 0.067MAST 0.0015 0.0039 0.00LAME 0.0006 0.0001 0.00CALVING INT 0.37 0.79 0.00NR56 -0.0027 -0.0047 0.00SCC -0.0045 -0.0005 -0.0046

Economic response £/cow £7.11 £3.21 £6.80Environmental response kg CO2e/cow -33.50 -64.07 -20.52

While illustrative, it is important to note that whole production chain emissions estimates, such as LCA indicators, do not translate obviously into policy instruments that government can use to affect lower emissions practices such as breed selection and breeding goals. In other words, the emissions intensity of a unit of product includes emissions that are located along different sections of the production chain in a series of points that do not lend themselves to easy regulation. It is feasible for these total emissions to be targeted by market price differentials at their endpoint; retailers choose to supply (or not) or differentially price goods according to the life cycle emissions intensity of relevant goods. But this does not necessarily transmit into breeding decisions on farm. As an alternative, government might also choose a practical point of obligation (e.g. within the farm gate, transport or processing) to regulate by command and control or a market-based instrument. . On the basis of the fact that within farm gate emissions are highest and that farmers provide a clear point of obligation, it makes sense to target market incentives/forces at this part of the production chain. Farmers can then choose management interventions (including breeding decisions) to minimise their emissions subject to regulatory instruments in force.

While a carbon tax is the clearest instrument for implementing the polluter pays principle, the feasibility of an emissions trading scheme is favoured for the property of delivering a certain cap on emissions. In theory, both approaches can be shown to deliver identical outcomes, although differences in transaction, monitoring and enforcement costs are likely to remain a significant hurdle in terms of the farm/producer size threshold, below which a scheme is unviable. Nevertheless it is possible to simulate results under alternative carbon price assumptions.

Figure 6.1 shows how the overall economic and environmental performance changes moving from the current index weights to indices which include environmental weights at increasing emphasis. It should be noted that in all cases except hill sheep scenario that the current index, which is based on the economic performance of the system has a favourable impact on GHG emissions, reducing them year on year. Combining the current index weights with environmental weights, even at a low level

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(Eco_a) increases the expected annual and cumulative response in reduction of GHG emissions with a relatively low economic impact. For example, in dairy the index weights Eco_a (current weights + environmental weights*£12/t CO2e) improves the expected response in the reduction of GHG emissions by 15% with only a 0.5% reduction in overall economic response. This trend is consistent across all of the breeding scenarios studied.

Figure 6.1. Percentage change in expected responses, relative to current index, in current economic performance, change in GHG emissions and “future” economic performance with a shadow price of carbon of £32.90 (2020 price) of indices that combine current weights with environmental weights (Eco-d).

The scale of the expected improvement in the rate of GHG emissions reductions within a breeding scenario indicates how close the current index weights are to environmental index weights. In the hill sheep and terminal sheep breeding scenarios there are large relative increases in the GHG emissions reduction potential when environmental weights are incorporated in the weights, four and ten fold improvement relative to the current index respectively (Figure 6.1). This highlights that the current index weights focus on a selection of relevant traits to that breeding scenario. For example, the current index weights in terminal sheep breeds focus solely on production traits and do not include traits related to fitness traits. This reflects the relative importance of production traits to the overall economic performance in terminal sheep breeds. However, it is likely that a wider range of traits impact on the

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economic performance of both pedigree terminal sheep breed flocks but also in terms of the use of terminal purebred sires in commercial flocks. The addition of a wider range of traits that relate to both production and system efficiency in the overall economics of both he purebred flocks and the commercial flocks and in deriving index weights is likely to bring the economic index weights more closely aligned to environmental goals.

The UK government has signalled its desire to reduce emissions efficiently with a preference for market-based methods (i.e. emissions trading formats) currently under investigation for their feasibility in agriculture. Evaluation of emissions reductions in all sectors has been facilitated by the development of the SPC described earlier. But due to the structure of agriculture (i.e. many small producers), it is currently uncertain how the SPC or a trading regime may operate (Nera, 2007). For the sake of this project we have assumed that the carbon responsibility of a farmer lies within the farm gates, and that permits may have to be held by livestock producers to account for emissions inside the farm gate. What this means is that producers will either have to participate in the emissions trading scheme (ETS) by buying permits at the prevailing price, or that a sub-sector scheme (e.g. among livestock producers) might operate outside the ETS. In this scheme, after an initial allocation (by auction or free allocation) emitters can either hold or sell their permits depending on the relative costs to reduce emissions. Depending on permit prices, some producers will opt to use an index to help select low emissions systems at low cost, and therefore be able sell on the permit. Others will not have the cost cutting option and will therefore have to buy allowances. The permit represents a notional price (or cost) to buyers and sellers that should therefore drive the selection of low emissions animals and thus the breeding requirement. In the absence of this price there is potentially limited incentive to make these breeding decisions. The only alternative to this polluter pays objective might be to implement some provider gets form of incentive by including certified breeding decisions in the portfolio of activities Axis 2 agri-environmental measures allowable under EU Rural Development Regulations.

In the future, carbon emissions may have a direct economic cost/benefit to the system, either via a shadow price of carbon, carbon taxing or an emissions trading scheme. Breeding decisions impact on the future performance of the system (i.e., when the offspring of a given mating is producing) and therefore it is important that the selection indices of today are aligned with future production systems, breeding scenarios and economic circumstances. Figure 6.1 shows the expected “future” economic response if GHG emissions had an economic value of £32.90/t CO2e (2020 shadow price of carbon) across a range of index weights with increasing emphasis of environmental impact relative to economic performance. The results show that at this price of carbon the overall economic performance can be increased by up to 2% in all scenarios expect hill sheep where it could be increased by up to 33% relative to the current index. Even with the small impact on economic performance, there is a large benefit in terms of reduction of GHG emissions. These results highlight that the mechanisms to incentivise producers to reduce GHG emissions would have both a favourable economic impact on the livestock system as well as a large and favourable impact in terms of the rate at which GHG emissions are reduced from livestock systems.

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7. Conclusions and Recommendations

The results of this study show that the majority of current breeding goals in ruminant and monogastric species have a favourable impact on GHG emissions, except for hill sheep systems. Many of the current breeding goals in livestock species have more recently moved towards focussing on both production and system efficiency, and the traits that underlie them (Section 2). This is because both production and system efficiency impacts on the economic performance of the production system and therefore have been included in the selection index. This has a favourable knock-on effect on reducing GHG emissions as the overall production and system efficiency are improved.

One of the limitations to the continued addition of traits that relate to production and system efficiency is the lack of information recorded on these types of traits across a range of UK production systems. For example, Section 4 describes how residual feed intake could be added to beef and sheep indices in the UK and will improve the rate of GHG emissions reduction. It is also likely to have a positive impact on the overall economic performance of beef and sheep systems. However, there is limited information recorded on feed intake in beef or sheep in experimental circumstances and none recorded routinely in the field and across UK production systems. Feed intake it an expensive trait to record in practice and therefore may prove prohibitive to include in selection indices in the traditional manner. Newer genetic technologies such as genome wide selection and potential development of cheaper recording tools could enhance both the economic and environmental performance of livestock systems if included in selection indices. This also applies to other traits that are difficult to record in practice such as health traits. Some of these issues are discussed in a Defra Report (FFG0149).

The results show that environmental index weights can be calculated for ruminant production systems and that selection of such an index would greatly improve the rate of reduction in GHG emissions with a relatively small decrease in overall economic performance. 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 (Figure 4.2). This may conflict with some of the other issues that livestock producers face as increasing the weighting on production traits can have a unfavourable impact on fitness traits. This may be contrary to some wider societal requirements of improving health and welfare of livestock on farms. This study has described some of the trade-offs that could occur between economic, environmental and animal health and welfare outputs from livestock systems when breeding goals need to consider a multi-faceted range of outputs. However, it must be noted that not of the expected response in health and welfare traits were consistently favourable or unfavourable (i.e., with a given index some health and welfare traits changed in a favourable manner and others changed in an unfavourable manner). The relationships between some of these more difficult to measure traits and other traits in the index is less well studied and therefore it is difficult to consider the full correlated effects when selecting on different indices. To fully understand the trade-offs that may occur with conflicting demands on livestock keepers it is important to understand the relationships between all relevant traits. This requires developing recording/measurement protocols that can be used in practise, estimating genetic and phenotypic relationships between all the traits and understand how the traits effect overall performance (e.g., economic and environmental) of the livestock system.

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Although a range of sheep and beef systems are were used to help calculate environmental weightings an “average” system was used to describe a representative system. The impact of improving both production and system efficiency traits on GHG emissions (and economic performance) is likely to vary across a range of systems. To develop models that apply across a range of UK production systems it would be necessary to define these systems in detail (e.g., animal performance, feeding practices, manure management), measure/model the impact of different system management practices, including changes in animal performance, on GHG emissions and the knock-on consequences on overall system performance (e.g., production output, costs, input requirements). Developing such techniques would help develop more targeted genetic improvement regimes allowing farmers make breeding decisions that are system specific and as such improve the rate of improvement in economic performance and reduction in GHG emissions.

The results of this study show that improving production efficiency can have one of the largest impacts on reducing GHG emissions from ruminant systems. It will also have a large economic benefit. Although improving some fitness traits, and as such system efficiency, was also shown to have some stand-alone favourable impacts on GHG emissions it does not out perform chasing production efficiency. However, having a heavy focus on production efficiency alone in a breeding objective is known to have a negative impact on the fitness traits. Also, selection on production efficiency will increase the amount of feed required to maintain that production level. The Genetic GHG model did account for the additional carbon costs of including non-home produced food (both silage and concentrate ration) when production levels, both in terms of overall output and/or time to finish, increased. However, the model did assume that the additional food energy requirements were available and as such did not limit the production potential of animals. In the near future, there may well be competition for the resources that have traditionally gone into ruminant diets (e.g., cereals for human consumption rather than ruminant consumption, competition with monogastric animals). This highlights the requirement to explore the limitations that may exist to chase emissions reductions targets only. This could be examined by limiting the amount of external feed energy available in the Genetic GHG model and see if the production efficiency relative to system efficiency has such a large impact on reducing GHG emissions. Examining the impact of alternative sources of ruminant feed energy from off-farm sources could also be incorporated, considering the benefit of by products and residues as a dietary energy source.

One of the other limitations to ruminant production systems in the future may well be that some of the high quality pasture land in the UK that has traditionally supported ruminant production systems, particularly dairy, would be suitable for producing crops for human food and bio-fuels and therefore challenge this type of ruminant system. However, these future land-use challenges do not necessarily preclude ruminant production. In fact, ruminant animals provide a route to producing food for humans from land not suitable for crop production. Also under climate change scenarios there is potential for currently non-prime agricultural land to have increased production potential (Figure 7.1) and therefore increase not only crop yields on prime categories of land but also increase area of, and grass growth potential on, those less than prime categories. Matching alternative future land use scenarios to breeding objectives could help ruminant producers to adapt to the potential challenges from alternating land use strategies. To optimise breeding goals to fit with land use strategies it may be necessary to develop a breeding goal that works across species, balancing the goals all livestock species to the potential mapped land uses, rather than considering individually within species.

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Figure 7.1. Maps of land capability in Scotland “today” (a) and in the 2050s (b) with climate change under the medium-high UKCIP02 emissions scenarios. Classes of land capability ranges from Class 1 = prime agricultural land (suitable for range of crops) to Class 7 = very little agricultural value. © Macaulay Land Use Research Centre (adapted from Brown et al., 2008).

This study has developed a model for estimating the impact of biological change in livestock in GHG emissions from ruminant systems. This has included as many production and system efficiency traits as is possible to consider with systems of modelling GHG emissions. However, there are traits that are likely to impact on system efficiency, and therefore GHG emissions, which are difficult to include in this framework due to lack of information. For example, some of the animal health traits may affect system efficiency above and beyond animal survival in the system that has been incorporated in the GHG emissions model. For example, in dairy cattle mastitis is an important trait economically that impacts on system profit in terms of the involuntary culling of dairy cows for health reasons but also in terms of lost income when milk of mastitic cows has to be withdrawn from the system. The impact of mastitis on involuntary culling rate is incorporated in the GHG model via the weight calculated for lifespan. However, there are no studies on the impact of individual diseases, such as mastitis in dairy cattle, on GHG emissions from livestock either by a direct and real reduction in GHG emissions for a given level of food intake and/or by altering the animal’s motivation to feed. Therefore it is difficult to incorporate the direct effects of some of the potential traits on overall system emissions. To fully incorporate such impacts requires further studies on the impacts of different traits on animal performance and GHG emissions.

The Genetic GHG model developed in this project is more complex than systems currently used in GHG emissions inventory in the UK and therefore some of the estimated changes in emissions as a result of changes in biological traits and/or as a result of alternative breeding goals may not be fully or directly reflected in the current UK inventory mechanisms. However, the models developed are in line with IPCC Tier II/III mechanisms and therefore, in the future, it is hoped that developments to UK inventory would allow these more detailed and complex emissions changes to be incorporated.

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This study has shown that many of the currently available selection indices in livestock bring about GHG emissions reduction in livestock systems. However, to fully realise this impact would require farmers to utilise selection information available (e.g., estimated breeding values and selection indices) when making breeding decisions on farm. Also, breeding tools are based on phenotypic and pedigree records that require active recording on farm. In monogastric systems there tends to be integrated production systems with breeding companies and therefore data recording (and feedback) and breeding tools are successfully implemented through to commercial production systems. However, the ruminant industry, particularly beef and sheep, is more diffuse and is made up on many individuals (pedigree societies, breeding companies, breeders, individual farmers) that may not be actively participating in a common set of breeding goals. Studies have shown that the benefit, both economically (e.g., Amer et al., 2007) and environmentally (e.g., Jones et al., 2008) of improved dissemination of performance recording and genetic improvement throughout the ruminant production systems, but yet not all commercial farmers are utilising such information. Understanding and removing the barriers to uptake of current and new technology and information, including genetic tools, on farm is important to help fully realise the potential of these tools. The potential impact of differing rates of the dissemination of genetic improvement in ruminant populations, and the cost-effectiveness of alternative uptake rates, is discussed in Defra project IF0149 (Determining strategies for delivering environmentally sustainable production in the UK ruminant industry through genetic improvement).

This study has shown that some of the potential mechanisms that could be put in place to bring about GHG emissions reductions (e.g., carbon taxing/pricing) could incentivise farmers to bring about further emissions reductions above and beyond those that could be achieved by selecting on currently available indices. The “value” of carbon has to be high (£100/t CO2e) to begin to have major impacts on the general manner in which current selection indices have been altering considering both production (e.g., product output) and system (e.g., health, fertility, longevity) efficiency in their selection indices. This suggests that many of the potential mechanisms that may be put in place will not dramatically alter the focus of breeding goals, but rather redistribute the relative weightings/pressures on traits in the selection index and/or add traits that relate more closely to the goal of efficiency and reduced emissions. It should be noted that the environmental weightings and pricings were developed with the point of obligation for GHG emissions sitting within the farm-gate (i.e., each individual farmer is responsible/accountable for the emissions that occur with the farm gate only). However, it is still uncertain where, and with who, the point of obligation for emissions from agriculture will sit (e.g., consumers, retailers, agricultural bodies, government) and what will be included (e.g., transport to and from farms, land-use change, carbon sequestration management, displacement of land overseas). These many complex issues are still be debated and researched. However, once valuations and decisions have been reached it would be theoretically possible to include some of the ancillary costs and off-farm emissions cots in the framework developed in this study.

This project has shown that, on the whole, current selection objectives in UK livestock species 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. 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

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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 for help UK livestock agriculture meet the challenges of the reducing GHG emissions.

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AcknowledgementsFunding for this study was gratefully received from Defra. The authors would like to acknowledge the input to aspects of this work from John Vipond and Kev Bevan (SAC, Consultancy Division); Michael MacLeod, Kirsty Moore, Mike Coffey, Elly Navajas, Rami Swalaha and Carol-Anne Duthie (SAC, Research Division); Geoff Pollott (Royal Veterinary College); and Adrian Williams (Cranfield University).

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Appendix 1. Greenhouse gas emissions associated with selected sheep, beef and dairy feed types

Adapted from Cranfield LCA model, version 3, Williams et al, 2007. the global warming potential (GWP) of different diet types expressed in CO2e

Stock type

Organic status

Feed type GWP (100) kg CO2 e/MJGE

Sheep Non-organic Lowland pasture 0.0081Sheep Non-organic Upland pasture 0.0086Sheep Non-organic Lowland silage/hay 0.0208Sheep Non-organic Upland silage/hay 0.0209Sheep Non-organic Concentrates 0.0376

Beef Non-organic Hill grazing pasture 0.0051Beef Non-organic Upland silage/hay 0.0205Beef Non-organic Concentrates 0.0376

Dairy Non-organic Lowland pasture 0.0176Dairy Non-organic Lowland silage 0.0163Dairy Non-organic Concentrates 0.0165

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Appendix 2. UK sheep model farms performance data

Performance trait

Hill Reference Uplands Reference Lowlands Reference

EwesNo. ewes 100 Conington et

al. (2004)100 Conington et

al. (2004)100 Conington et

al. (2004)Mature weight ewes (kg)

50 Conington et al. (2004)

73 Haresign et al. (2007)

75 Jones et al. (2004)

Mature weight rams (kg)

65.0 Based on 1.3 times ewe mature wt (extrapolation of Jones et al. (2004) who mentioned castrates were 1.1 times higher than ewes MW

94.9 Based on 1.3 times ewe mature wt (Jones et al. (2004) mentioned castrates were 1.1 times higher than ewes MW

97.5 Based on 1.3 times ewe mature wt (Jones et al. (2004) mentioned castrates were 1.1 times higher than ewes MW

No. ewe deaths pa/100 ewes

4.5 Conington et al. (2004)

3.0 Haresign et al. (2007)

3.0 Haresign et al. (2007)

No. barren ewe culls pa /100 ewes

7.0 Conington et al. (2004)

7.0 Haresign et al. (2007)

7.0 Haresign et al. (2007)

No.other ewe culls pa / 100 ewes

11 SAC(2007) 15 SAC(2007) 15 SAC(2007)

Cull ewe Carcase weight (kg)

25 36 37.5

Ewe Prolificacy %

111 Conington et al. (2004)

175 SAC, (2007) 193 Haresign et al. (2007)

Ram to breeding ewe ratio

0.03 SAC, (2007) 0.03 SAC, (2007) 0.03 SAC, (2007)

Gimmer death + cull %

6 SAC, (2007) pp 172

6 SAC, (2007) pp 172

6 SAC, (2007) pp 172

LambsBirth weight (kg)

3.9 Conington et al. (2004)

4.5 Haresign et al. (2007)

4.7 Jones et al. (2004)

No. lambs born/100 initial ewes

101 Conington et al. (2004)

160 Haresign et al. (2007)

177 Jones et al. (2004)

Average lamb survival (birth to wean) (%)

89 Conington et al. (2004)

85 Haresign et al. (2007)

83 Haresign et al. (2007)

No. lambs weaned/100 initial ewes

90 Conington et al. (2004)

136 Haresign et al. (2007)

147 Haresign et al. (2007)

Target lamb carcase weight (kg)

16.8 Conington et al. (2004)

18.9 Haresign et al. (2007)

18.9 Haresign et al. (2007)

Slaughter yield (% of

43 Conington et al. (2004)

44.8 Haresign et al. (2007)

45 Jones et al. (2004)

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live-weight as carcase)Live weight gain (birth to wean) kg/day

0.187 Conington et al. (2004)

0.210 Haresign et al. (2007)

0.230 Jones et al. (2004)

Live weight gain (wean to slaughter) kg/day

0.120 Conington et al. (2004)

0.160 SAC consultants

0.180 SAC consultants

Age at weaning (days)

150 SAC consultants

122 SAC consultants

122 SAC consultants

Age at slaughter (days)

208 SAC consultants

198 SAC consultants

173 SAC consultants

Single lamb survival (birth to wean) %

90 Conington et al. (2004)

90 Conington et al. (2004)

90 Conington et al. (2004)

Twin lamb survival (birth to wean) %

85 Conington et al. (2004)

85 Conington et al. (2004)

85 Conington et al. (2004)

Triplet lamb survival (birth to wean) %

70 Conington et al. (2004)

70 Conington et al. (2004)

70 Conington et al. (2004)

FarmAnnual pasture production (kg DM/ha pa)

4515 Common et al (1990)

11500 Dale and Moore (2007)

14000 Thomas et al (1991)

Average winter temperature (ºC)

3.7 Met office (2008)

3.7 Met office (2008)

3.7 Met office (2008)

Average pasture quality (MJGE/kg DM)

18.45 IPCC, (2006) 18.45 IPCC, (2006) 18.45 IPCC, (2006)

Crude Protein in diet (%)

18 De Ruiter, et al. (2007)

18 De Ruiter, et al. (2007)

18 De Ruiter. et al. (2007)

% of days on farm ewes are fed indoors

0 0 11.5 SAC consultants

% of days on farm lambs are fed indoors

0 0 0 Jones et al. (2004)

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Appendix 3. Methane conversion factors for sheep and cattle

Class of stock Methane conversion factors (Ym) (IPCC 2006)

Lambs (<1 year old indoors) 3.5%

Lambs (<1 year old outdoors) 4.5%

Adult sheep (>1 year) indoors 5.5%

Adult sheep (>1 year) outdoors 6.5%

Beef cattle (indoors)(for diet over 90% concentrates)

3.0%

Dairy/Beef cattle (on pasture) 6.5%

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Appendix 4. UK beef model farms performance data

Performance trait UK beef model ReferenceCowsNo. cows 100Mature weight (kg) cows 600 Roughsedge et al (2005)

using hill systemMature weight (kg) bulls 780 About 1.3 times more than

cow(no ref)No. cow deaths 2 SAC (2007)-erring on highNo. Barren cow culls 3 SAC (2007)- erring on highNo. Other cow culls 12 SAC (2007)- back calc’d

using cost of replace per cow pp 145

Cull cow Carcase weight (kg) 300 Roughsedge et al. (2005) based on 50% DO%

Bull to breeding cow ratio (bulls per cow)

0.03 SAC (2007) pp 150

Heifer death + cull % 6% of 2 year heifers that produce a live calf to weaning

50 Roughsedge et al. (2005)

% of 3 year cows that produce a live calf to weaning

85 Roughsedge et al. (2005)

% of 4+ year cows that produce a live calf to weaning

90 Roughsedge et al. (2005)

Cow milk production (kg/day) 4.2 Mallinckrodt et al. (1993)

CalvesBirth weight (kg) 40 Roughsedge (2005) Age at weaning (days) 210d Roughsedge et al (2005)

straight from model (pure native)

Live weight gain- birth to wean (kg/day)

0.80 Roughsedge et al (2005) back calc’d using pure native

Live weight gain- post weaning (kg/day)

0.75 Roughsedge et al (2005) straight from model (pure native) (back calculated)

Calf survival from wean to slaughter (%)

98 Roughsedge et al. (2005) direct from model

Target carcase weight (kg) 312kg Roughsedge et al. (2005)Slaughter yield % (Cwt as a % of slaughter Lwt)

52 Roughsedge et al (2005)-direct from model

Age at slaughter (days post partum)

733 Roughsedge et al (2005) assumed about a typical 24 month finish period

FarmAnnual pasture production (kg

DM/ha pa)4515- assume they are in hills

Common et al (1990)

Average winter temperature (ºC) 3.7 Metoffice (2008)Average pasture quality (MJGE/kg

DM) 18.45 IPCC, (2006)

Crude Protein in diet (%) 18 De Ruiter et al (2007) % of days on farm cows are fed

indoors50 Smith et al (2001)

% of days on farm calves are fed indoors

50 Smith et al (2001)

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Appendix 5. UK dairy herd performance data

Input Value UnitsBirth weight 45 kgWeight at 1st calving 540 kgMature weight 625 kgsNumber of cows 125 countQuota 750,000 litresDays indoors 190 days1st lact yld 7750 kgs2nd lact yld 8605 kgs3rd+ lact yld 8973 kgsFat percent 3.8 %%DE pasture 65 %%DE feed indoors 75 %Prop cows that don't calve 10 %Heifer preg rate of hefers pregnant 70 %Calf to heifer mortality 2 %Age at first calving 730 daysReplacement rate 25.8 %Land 80 haNitrogen per hectare 225 g/haCalving interval 370 daysDays in milk 307 daysGestation 280 daysUrinary energy expressed as fraction of GE 0.04 fraction of GEAsh content of manure 0.08 fraction of DMIMethane conversion factors for grazing dairy cows 1.5 %Methane conversion factor indoor dairy cows 17 %Methane conversion factors for grazing young stock 1.5 %Methane conversion factor indoors for young stock 17 %(Bo) Max CH4 producing capacity for manure for dairy cows 0.24 m^3CH4/kg VS(Bo) Max CH4 producing cap. for manure for young stock 0.18 m^3CH4/kg VSShadow price of carbon 26.5 £/t C eq.N ex factor dairy cows 116.2 kg N/animal/year1-12 month N ex factor 3.8 kg N/animal/year12-24 pregnant heifer N ex factor 67 kg N/animal/year12-24 non-pregnant heifer N ex factor 38 kg N/animal/yearGWP CH4 (IPCC 4th AR) 25 CO2 eqGWP N2O (IPCC 4th AR) 298 CO2 eq

Turn out date 15th AprilTurn in date 15th Sept

GWP grass diet 0.33 kg CO2 e/kgDMGWP conc diet 0.39 kg CO2 e/kgDMGWP silage 0.3 kg CO2 e/kgDM

DM from grass grazing 50 kg/ha/d

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Appendix 6. Calculation of Discounted Genetic Expressions Coefficients

This section describes in detail the calculations of discounted genetic expression coefficients.

Definitionse is the number of ewes mated per sire (=60)vBF is the average number of crossbred lambs sold for slaughter or reaching replacement age per ewe hill ewe joined (=1.27)u is the proportion of surviving female crossbred lambs kept as replacements (=0.8)lsts is the average survival from birth to slaughter for crossbred lambs r is the discount rate (=0.07)rs is the survival rate of rams following their first year of use (=0.82).D is a vector of probabilities of a crossbred ewe surviving to each year of age lagged by 1 year (DT=0,0,.84,.72,.60,.48,.36,0,0,0)G is a vector of probabilities of a crossbred ewe dieing at each year of age lagged by 1 year (GT=0,0,0,.12,.12,.12,.12,.36,0,0)H is a vector made up of all zero’s except a 1 at place two to indicate the expression of female replacement genesuNLB is the mean number of lambs born per crossbred ewe lambing (=1.92)pNB is the probability that a crossbred ewe is not barren at a lambing (=0.94)q is a vector of discount factors to account for delays due to the aging of ewes where

The number of discounted genetic expressions of a Crossing Sire’s genes in Crossbred ewes expressed annually by mature ewes

The number of discounted genetic expressions of a Crossing Sire’s genes in Crossbred ewes as they are reared to become replacement breeding ewes

The number of discounted genetic expressions of a Crossing Sire’s genes in Crossbred ewes at the end of their life.

The number of discounted genetic expressions of a Crossing Sire’s direct genes in commercial Crossbred lambs at birth:

The number of discounted genetic expressions of a Crossing Sire’s direct genes in commercial terminally sired lambs out of Mule ewes at birth

The number of discounted genetic expressions of a Crossing Sire’s maternal genes in commercial Crossbred lambs at birth:DGE_LBTH_M_CB=0

The number of discounted genetic expressions of a Crossing Sire’s maternal genes in commercial terminally sired lambs out of Crossbred ewes at birthDGE_LBTH_M_TS= DGE_LBTH_D_TS x 2 =140

The number of discounted genetic expressions of a Crossing Sire’s genes in commercial Crossbred lambs at slaughterDGE_LSLT_CB= DGE_LBTH_D_CB x lsts x (1-0.5u) =50

The number of discounted genetic expressions of a Crossing Sire’s genes in commercial terminally sired lambs out of Crossbred ewes at slaughterDGE_LSLT_TS= DGE_LBTH_D_TS x lsts =56

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Appendix 7. Recorded trait responses

Hill Sheep: Recorded trait responses for hill sheep when seven different breeding objectives were selected

Breeding objectives and recorded trait responses (in trait units pa.)Trait names

Current Farm Profit Index

GHG1/brd female

GHG2/kg product

Eco+GHG1 weights with SPC of £12/t CO2e

Eco+GHG1 weights with SPC of £26.5/t CO2e

Eco+GHG1 weights with SPC of £32.9/t CO2e

Eco+GHG1 weights with SPC of £100/t CO2e

8WK- Direct

0.315 -0.123 -0.118 0.303 0.258 0.235 0.032

8WK- Maternal

0.000 0.000 0.000 0.000 0.000 0.000 0.000

SWT 0.539 -0.595 -0.581 0.416 0.215 0.134 -0.358MD 0.012 -0.189 -0.198 -0.037 -0.098 -0.118 -0.194FD -0.014 -0.055 -0.058 -0.028 -0.044 -0.049 -0.061MAT- Maternal

0.083 -0.113 -0.108 0.058 0.020 0.005 -0.079

MS- Direct

0.558 -1.227 -1.199 0.267 -0.138 -0.287 -1.019

LSR- Direct

0.019 -0.022 -0.021 0.014 0.007 0.004 -0.014

RFI-test 0.033 0.007 0.009 0.036 0.037 0.036 0.021Ewe longevity

0.003 0.006 0.043 0.005 0.006 0.007 0.008

Footrot 0.002 -0.004 -0.004 0.001 0.000 -0.001 -0.003FECS -0.019 0.005 0.005 -0.019 -0.017 -0.016 -0.004FECN 0.021 -0.005 -0.005 0.021 0.019 0.018 0.005Lamb survival, direct

0.001 0.000 0.000 0.001 0.001 0.001 0.000

Crossing (Upland) sheep: Recorded trait responses for crossing (upland) sheep when seven different breeding objectives were selected

Breeding objectives and recorded trait responses (in trait units pa.)Current Farm Profit Index

GHG1/brd female

GHG2/kg product

Eco+GHG1 weights with SPC of £12/t CO2e

Eco+GHG1 weights with SPC of £26.5/t CO2e

Eco+GHG1 weights with SPC of £32.9/t CO2e

Eco+GHG1 weights with SPC of £100/t CO2e

Trait name8WK- Direct

-0.197 -0.029 -0.030 -0.181 -0.160 -0.152 -0.099

8WK- Maternal

-0.057 -0.027 -0.028 -0.056 -0.054 -0.052 -0.043

SWT -0.158 0.446 0.451 -0.049 0.052 0.088 0.273MD 0.206 0.172 0.163 0.217 0.222 0.222 0.210FD -0.012 0.102 0.098 0.010 0.030 0.037 0.072MAT- Direct

-0.018 -0.003 -0.003 -0.017 -0.015 -0.014 -0.010

MAT- Maternal

-0.001 0.000 0.000 -0.001 -0.001 -0.001 -0.001

MS -0.095 0.042 0.044 -0.075 -0.056 -0.048 -0.006LSB- Direct

0.003 0.004 0.004 0.004 0.004 0.004 0.004

86

CS -0.010 0.057 0.055 0.002 0.014 0.018 0.039RFI-test 0.001 -0.037 -0.036 -0.007 -0.014 -0.016 -0.028Longivity -0.010 -0.013 0.010 -0.011 -0.013 -0.013 -0.014Footrot -0.006 0.001 0.000 -0.006 -0.005 -0.004 -0.002FECS 0.008 0.001 0.001 0.007 0.006 0.006 0.004FECN 0.004 -0.001 -0.001 0.003 0.003 0.002 0.001Lamb Survival-Direct

-0.001 0.000 0.000 -0.001 -0.001 -0.001 -0.001

Terminal (Lowland) sheep: Recorded trait responses for terminal (lowland) sheep when seven different breeding objectives were selected

Breeding objectives and recorded trait responses (in trait units pa.)Current Farm profit Index

GHG1/brd female

GHG2/kg product

Eco+GHG1 weights with SPC of £12/t CO2e

Eco+GHG1 weights with SPC of £26.5/t CO2e

Eco+GHG1 weights with SPC of £32.9/t CO2e

Eco+GHG1 weights with SPC of £100/t CO2e

Trait names8 WK-Direct

0.029 0.152 0.156 0.031 0.034 0.034 0.043

8 WK-Maternal

0.004 0.021 0.021 0.005 0.005 0.005 0.005

SWT 0.020 0.107 0.120 0.028 0.038 0.042 0.080MD 0.222 -0.025 -0.026 0.221 0.218 0.217 0.202FD -0.038 0.004 0.003 -0.038 -0.038 -0.038 -0.038MAT-Maternal

-0.004 0.010 0.010 -0.004 -0.004 -0.003 -0.002

MS 0.013 0.116 0.116 0.016 0.019 0.020 0.034LSR 0.011 0.001 0.002 0.011 0.011 0.011 0.012CT_L 0.154 0.036 0.039 0.157 0.159 0.160 0.165CT_F -0.118 0.063 0.066 -0.116 -0.112 -0.110 -0.093FECS -0.004 -0.006 -0.007 -0.005 -0.005 -0.005 -0.006FECN 0.022 0.004 0.005 0.022 0.022 0.022 0.023RFI-test 0.046 -0.011 -0.009 0.048 0.049 0.050 0.055Lamb Survival-Direct

0.001 0.000 0.001 0.001 0.001 0.001 0.001

Maternal beef: Recorded trait responses for maternal beef cattle when seven different breeding objectives were selected

Breeding objectives and recorded trait responses (in trait units pa.)Current Farm profit Index

GHG1/brd female

GHG2/kg product

Eco+GHG1 weights with SPC of £12/t CO2e

Eco+GHG1 weights with SPC of £26.5/t CO2e

Eco+GHG1 weights with SPC of £32.9/t CO2e

Eco+GHG1 weights with SPC of £100/t CO2e

Trait namesBWT-direct

0.041 0.088 0.091 0.046 0.046 0.054 0.068

BWT-maternal

0.008 0.001 0.002 0.007 0.007 0.007 0.005

WT200-direct

-0.700 0.471 0.338 -0.607 -0.607 -0.468 -0.174

87

WT200-maternal

0.169 0.031 0.028 0.160 0.160 0.146 0.114

WT400 0.194 1.955 1.729 0.368 0.368 0.615 1.094MSC 0.012 0.026 0.026 0.014 0.014 0.016 0.020FD 0.604 0.071 -0.127 0.569 0.569 0.514 0.387MD 1.404 1.913 1.933 1.491 1.491 1.604 1.781GL-direct

-0.032 0.043 0.040 -0.026 -0.026 -0.016 0.003

GL-maternal

0.002 -0.004 -0.003 0.001 0.001 0.000 -0.001

CD-direct

-0.004 -0.006 0.000 -0.004 -0.004 -0.004 -0.005

CD-maternal

-0.011 -0.015 -0.015 -0.012 -0.012 -0.013 -0.014

CI -0.634 0.410 -0.346 -0.551 -0.551 -0.427 -0.164AF -0.004 -0.009 -0.003 -0.004 -0.004 -0.005 -0.007LS 0.036 0.010 0.013 0.034 0.034 0.032 0.026MW -4.107 -0.561 -1.154 -3.879 -3.879 -3.520 -2.687RFI-test -0.266 -3.519 -3.551 -0.586 -0.586 -1.040 -1.923SF 0.001 -0.001 -0.001 0.001 0.001 0.000 0.000BSV-direct

0.000 0.000 0.000 0.000 0.000 0.000 0.000

BSV-maternal

0.000 0.000 0.000 0.000 0.000 0.000 0.000

WSV-direct

0.000 0.000 0.000 0.000 0.000 0.000 0.000

WSV-maternal

0.000 0.000 0.000 0.000 0.000 0.000 0.000

DS 0.008 0.008 0.009 0.009 0.009 0.009 0.009

Terminal beef: Recorded trait responses for terminal beef cattle when seven different breeding objectives were selected

Breeding objectives and recorded trait responses (in trait units pa.)Current Farm Profit Index

GHG1/brd female

GHG2/kg product

Eco+GHG1 weights with SPC of £12/t CO2e

Eco+GHG1 weights with SPC of £26.5/t CO2e

Eco+GHG1 weights with SPC of £32.9/t CO2e

Eco+GHG1 weights with SPC of £100/t CO2e

Trait namesBWT-direct

0.044 0.107 0.106 0.091 0.091 0.091 0.098

WT200 1.131 3.015 3.020 2.315 2.315 2.315 2.570WT400 2.393 5.078 5.063 4.834 4.834 4.834 5.033MSC 0.037 0.058 0.057 0.074 0.074 0.074 0.072FD -0.011 0.270 0.181 -0.007 -0.007 -0.007 0.069MD 1.385 2.541 2.527 2.778 2.778 2.778 2.788GL-direct

0.008 0.068 0.066 0.019 0.019 0.019 0.033

CD-direct

0.003 0.012 0.012 0.005 0.005 0.005 0.007

RFI-test

0.023 -3.039 -3.180 -0.107 -0.107 -0.107 -0.913

SF 0.000 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001BSV-direct

0.000 0.000 0.000 0.000 0.000 0.000 0.000

WSV- 0.000 0.000 0.000 0.000 0.000 0.000 0.000

88

directDS 0.005 0.006 0.006 0.009 0.009 0.009 0.009

89

Appendix 8. Responses in hill sheep when RFI environmental weight set to zero

Recorded trait responses for hill sheep RFI to zero when seven different breeding objectives were selected

Breeding objectives and recorded trait responses (in trait units pa.)Current Farm Profit Index

GHG1/brd female

GHG2/kg product

Eco+GHG1 weights with SPC of £12/t CO2e

Eco+GHG1 weights with SPC of £26.5/t CO2e

Eco+GHG1 weights with SPC of £32.9/t CO2e

Eco+GHG1 weights with SPC of £100/t CO2e

Trait names8WK- Direct

0.315 -0.123 -0.119 0.303 0.257 0.234 0.032

8WK- Maternal

0.000 0.000 0.000 0.000 0.000 0.000 0.000

SWT 0.539 -0.595 -0.582 0.415 0.214 0.133 -0.356MD 0.012 -0.189 -0.198 -0.037 -0.098 -0.117 -0.193FD -0.014 -0.053 -0.056 -0.028 -0.045 -0.050 -0.063MAT- Maternal

0.083 -0.113 -0.109 0.058 0.020 0.005 -0.078

MS- Direct

0.558 -1.227 -1.200 0.267 -0.138 -0.286 -1.016

LSR- Direct

0.019 -0.022 -0.021 0.014 0.007 0.004 -0.014

RFI-test 0.033 0.005 0.007 0.037 0.038 0.037 0.023Ewe longevity

0.003 0.006 0.043 0.005 0.006 0.007 0.008

Footrot 0.002 -0.004 -0.004 0.001 0.000 -0.001 -0.003FECS -0.019 0.005 0.005 -0.019 -0.017 -0.016 -0.004FECN 0.021 -0.005 -0.005 0.021 0.019 0.018 0.005Lamb survival, direct

0.001 0.000 0.000 0.001 0.001 0.001 0.000

Profit trait responses for hill sheep RFI to zero when seven different breeding objectives were selected

Breeding objectives and profit trait responses (in trait units pa.)Current Farm Profit Index

GHG1/brd female

GHG2/kg product

Eco+GHG1 weights with SPC of £12/t CO2e

Eco+GHG1 weights with SPC of £26.5/t CO2e

Eco+GHG1 weights with SPC of £32.9/t CO2e

Eco+GHG1 weights with SPC of £100/t CO2e

Trait namesCfat 0.028 -0.072 -0.072 0.010 -0.013 -0.022 -0.062Cmus 0.174 -0.138 -0.134 0.148 0.101 0.080 -0.058MS 0.558 -1.227 -1.200 0.267 -0.138 -0.286 -1.016MAT 0.083 -0.113 -0.109 0.058 0.020 0.005 -0.078LSR-D 0.019 -0.022 -0.021 0.014 0.007 0.004 -0.0148WK- Direct

0.316 -0.123 -0.119 0.303 0.258 0.235 0.032

RFI-Lambs

0.033 0.005 0.007 0.037 0.038 0.037 0.023

RFI-Ewes

0.000 0.000 0.000 0.000 0.000 0.000 0.000

Ewe 0.003 0.006 0.043 0.005 0.006 0.007 0.008

90

longevityFootrot 0.002 -0.004 -0.004 0.001 0.000 -0.001 -0.003FECS -0.019 0.005 0.005 -0.019 -0.017 -0.016 -0.004FECN 0.021 -0.005 -0.005 0.021 0.019 0.018 0.005Lamb survival, direct

0.001 0.001 0.001 0.001 0.001 0.001 0.000

Summary of Hill sheep RFI to zero annual selection response outcomes over seven breeding objectives

Breeding objectives and overall responses pa.Units Farm

profitGHG1 reduction per breeding female

GHG2 reduction per kg of product

Eco +GHG1 WTS £12/t CO2e

Eco +GHG1 WTS £26.5/t CO2e

Eco +GHG1 WTS £32.9t CO2e

Eco +GHG1 WTS £100/t CO2e

Current index

£/ewe £0.20 -£0.07 -£0.07 £0.20 £0.17 £0.15 £0.03

GHG reduction per ewe

kg CO2e/ewe

1.48 -4.18 -4.11 0.46 -0.92 -1.41 -3.68

GHG reduction per kg product produced

g CO2e/ kg product

127.44 -376.11 -382.60 35.38 -87.68 -131.52 -333.42

EU ETS 2009 carbon price (£12/t CO2e)

£/ewe £0.18 -£0.02 -£0.02 £0.19 £0.18 £0.17 £0.07

UK SPC 2009 (£26.50/t CO2e)

£/ewe £0.16 £0.04 £0.04 £0.18 £0.19 £0.19 £0.12

UK SPC 2020 (£32.90/t CO2e)

£/ewe £0.15 £0.06 £0.07 £0.18 £0.20 £0.20 £0.15

Worst case (£100/t CO2e)

£/ewe £0.05 £0.34 £0.35 £0.15 £0.26 £0.29 £0.39

91