Contents lists available at ScienceDirect

Livestock Science

Livestock Science 158 (2013) 1–11

1871-14http://d

n CorrE-m

journal homepage: www.elsevier.com/locate/livsci

Effect of incorporating greenhouse gas emission costsinto economic values of traits for intensive and extensivebeef cattle breeds

B.A. Åby a,n, L. Aass a, E. Sehested b, O. Vangen a

a Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norwayb GENO Breeding and AI Association, P.O. Box 5003, N-1432 Ås, Norway

a r t i c l e i n f o

Article history:Received 13 June 2012Received in revised form10 September 2013Accepted 13 September 2013

Keywords:Beef cattleBio-economic modelEconomic valuesFunctional traitsProduction traitsGreenhouse gas emission

13/$ - see front matter & 2013 Elsevier B.V. Ax.doi.org/10.1016/j.livsci.2013.09.009

esponding author. Tel.: þ47 6496 5134; faxail address: bente.aby@umb.no (B.A. Åby).

a b s t r a c t

Ruminants contribute considerably to the Greenhouse Gas (GHG) emissions from agricul-ture. Genetic improvements have a large potential through permanente and cumulativereductions in emissions. Currently, indirect selection through correlated traits consideredin broad breeding goals is the best option for reducing emissions. Breeding goal traits areweighed by their respective economic value (EV). The emission of GHG may be included inthe bio-economic model, and the costs of GHG emissions may be estimated and includedin the calculation of economic values using a shadow price. In this study emission costswere included in the calculations of economic values for two breed group under threeproduction conditions; (1) semi-intensive (2) completely roughage based (RB) and (3)minimum use of concentrates (MC). Three harvested roughage qualities (early, mediumand late cut) were included in the two latter situations, giving a total of 14 situations. EVwere estimated for seven functional traits: herd life of cow (HL), age at first calving (AFC),calving interval, stillbirth (S), twinning rate (T), calving difficulty, limb and claw disorders,and for seven production traits: birth weight, carcass weight, carcass conformation,carcass fatness, growth rate from birth to 200 days (weaning), growth rate from 200 to365 days and growth rate from 365 days to slaughter. Including GHG emissions intocalculation of economic values (EV) decreased the relative economic importance of thefunctional traits HL, AFC, S and T, while increasing the importance of the production traits.However, the overall effect of including GHG emission was small and little rerankingbetween the traits was observed. A sensitivity analysis for increased shadow price showedsmall effects on the EV. The results suggest that the economic values are robust towardsthe inclusion of GHG emission costs into the profit equation and also towards increasedshadow price. Thus, broad breeding goals for beef cattle including both production andfunctional traits do not need to be changed considerably to take the emission of GHG intoaccount.

& 2013 Elsevier B.V. All rights reserved.

1. Introduction

Agriculture contributes considerable to the anthropogenicGreen House Gas (GHG) emissions. In the European Union,agriculture is the second largest source of GHG emissions,

ll rights reserved.

: þ47 64 96 51 01.

accounting for 10% of the total emissions. Methane (CH4),nitrous oxide (N2O) and carbon dioxide (CO2) are the threemost important GHG (UNFCCC, 2011). Livestock, especiallyruminants, are important contributers to the amount of theemissions from agriculture (FAO, 2006). Therefore, livestockproduction plays an important role if GHG emissions fromthe agricultural sector are to be reduced.

Several management and feeding practices to reducemethane emissions have been proposed; e.g., feed additives,

B.A. Åby et al. / Livestock Science 158 (2013) 1–112

vaccination, diet manipulation and use of inoculants andacetogens (Cottle et al., 2011). Genetic improvements have alarge potential through permanent and cumulative reduc-tions in emissions. The aim of including mitigation inbreeding objectives should be to reduce the emission perkg of product and may be done in several ways (Wall et al.,2010). An obvious approach is direct selection for reducedemissions. Another approach is through indirect selectionby improving animal efficiency and productivity and redu-cing animal wastage (Wall et al., 2010). This indicates thedevelopment of broad breeding goals including both pro-duction and functional traits (Wall et al., 2010) whichconcur nicely to the current trend in breeding goal devel-opments in many breeds. Direct selection for reducedemissions suggests direct measurement on GHG emissionsfor large numbers of animals, which obviously is verytimeconsuming and expensive. Thus, indirect selectionbased on correlated traits already considered in broadbreeding goals seems currently to be the best option forincluding GHG emissions in breeding goals. However; thegenetic correlations between direct emissions and indicatortraits are currently unknown. Direct GHG emissions are alsoaround the corner, for example measuring methane emis-sions by using breath measurements (Lassen et al., 2012).

Usually, the traits included in the breeding objective areweighed by their economic value, which is estimated as thechange in profit from a unit change in the trait consideredusing e.g., bio-economic models (e.g., Phocas et al., 1998;Wolfova et al., 2005; Åby et al., 2012a). Likewise, GHGemissions are estimated using similar models, and thechange in emissions resulting from improvement in someof the considered traits are calculated (e.g., Beaucheminet al., 2011; Garnsworthy, 2004; Ogino et al., 2007). Tocalculate the cost of the GHG emissions from an environ-mental economics point of view, a shadow price per ton ofCO2-equivalent may be defined (DEFRA, 2007). Finally, byincorporating the GHG emission costs into bio-economicmodels, alternative economic values may be calculated (Wallet al., 2010). These authors used this approach to calculateenvironmental economic values in dairy cattle; however thisapproach has not yet been used in beef cattle.

Climate change and human population growth mayforce beef production into more extensive, roughage basedsystems due to limited availablility of grain (Beaucheminet al., 2010). This will influence the GHG emissions andthus their associated costs. Therefore, the effect of changedproduction conditions on the environmental economicvalues should be investigated.

The aim of this study was to incorporate the cost ofGHG emissions into the calculation of economic values forproduction and functional traits for beef cattle, using a bio-economic model considering both current and alternativeproduction conditions.

2. Materials and methods

2.1. The model, included traits, profit functions andproduction conditions

The deterministic model follows one suckler cow andprogeny through the cow's life cycle. Two breed groups are

defined; intensive (i.e., Charolais, Simmental and Limousin)and extensive (i.e., Hereford and Aberdeen Angus) withdifferences in performance, economy and other input fac-tors. The intensive breed group was characterised by higherperformance for the production traits and lower perfor-mance for the functional traits, and a higher proportion ofconcentrates and cultivated pasture in the diet. In addition ahigher proportion of labour was used for feeding andtending of animal and inspections during calving, comparedto the extensive breed group. In contrast, the extensivebreed group was defined by a lower performance for theproduction traits and higher performance for the functionaltraits, a larger amount of roughage and uncultivated pasturein the diet and a higher proportion of labour used forroughage harvesting, manure handling and maintenance ofbuildings and machinery (Åby et al., 2012a).

A total of 14 traits were included in the model: sevenproduction and seven functional traits. The productiontraits were: birth weight (kg), carcass weight (kg), carcassconformation (class), carcass fatness (group) and growthrate (gram per day) in three periods: (1) from birth toweaning at 200 days of age, (2) from weaning to 365 days,and (3) from 365 days to slaughter. The functional traitswere: herd life of cow (days), age at first calving (days),calving interval (days), calving difficulty (score), stillbirth(%), twinning frequency (%) and limb and claw disoders (%).

The profit function and calculation of economic valuesis described in detail in Åby et al. (2012a). Income comesfrom slaughter animals and subsidies. In the present study,the cost of GHG emissions is included in the profit functionin addition to the cost of feed, labour, calving difficulty andlimb and claw disorders. The marginal economic values(EVn) were estimated as the change in profit resulting froma 0.1% increase in the mean of the considered trait whilekeeping the mean of all other traits constant, and thenexpressed per unit increase of the trait (e.g., per kg or day).The economic values were expressed per suckler cow andyear. Relative economic values (REV, in %) were estimatedby using Eq. (1) (Åby et al., 2013)

REVi ¼jEVinsij

∑ni ¼ 1jEVinsij

� 100 ð1Þ

where si is the genetic standard deviation of the nth trait.Three alternative production conditions were investi-

gated in this study, one semi-intensive and two extensive.The semi-intensive was the basis situation (BAS) describedin Åby et al. (2012a). This production condition wascharacterised by intensive feeding of bulls and surplusheifers, using considerable amounts of concentrates and amore extensive suckler cow–calf enterprice. In addition,two extensive production conditions were included (Åbyet al., 2013); one completely roughage based production(RB) and one with minimum use of concentrates (MC).In both of the extensive situations, three roughage quali-ties were used: early (6.56 MJ NE/kg DM), medium(5.87 MJ NE/kg DM) and late cut (5.18 MJ NE/kg DM). Thisgave a total of 14 included situations. In the MC situation asmall amount of concentrates were given to bulls andreplacement heifers. Due to differences in feed intakebetween the included situations, the performance (i.e.,growth rate) of the bulls, surplus heifers and replacement

B.A. Åby et al. / Livestock Science 158 (2013) 1–11 3

heifers varied considerable. Because the feed requirementof the suckler cow according to the performance used byÅby et al. (2012a), was met in all included situatuins, theperformance of the suckler cow was kept constant (Åbyet al., 2013).

Total lifetime carcass yield per suckler cow for theintensive and extensive breed group for all includedsituations are shown in Table 1.

2.2. Calculation of GHG emission costs

The emissions were estimated in kg CO2-equivalents andcosts of these emissions were calculated by multiplying by ashadow price, which is a politically negotiated price forcarbon (0.035 EUR per kg of CO2-equivalents). The shadowprice was based on Aune et al. (1998), and adjusted to 2010value using the retail price index (Statistics Norway, 2010).The GHG emissions were converted into CO2-equivalentsby using the global warming potential (GWP) factors ofmethane and nitrous oxide of 21 and 310, respectively. TheGWP is a measure of the potential climate impact from oneunit emission of the GHG compared to carbon dioxide, whichis assigned a GWP of one (IPCC, 2007).

Estimates of emissions of GHG (CH4 and N2O) were basedmainly on the methodology described in the NorwegianEmission Inventory 2008 (Statistics Norway, 2008).

2.2.1. Methane from enteric fermentationIn general, the emission of methane was calculated

according to the formula from Statistics Norway (2008)

EFi ¼NinðGEinYmin365ðdays=yearÞÞ

55:54 MJ=kg CH4

� �nyri ð2Þ

where EFi is the emission factor of CH4 (kg CH4 perlifetime) for animal group i (i¼6), N is the number ofanimals, GE is gross energy intake (MJ/head and day) Ym isCH4 conversion rate and yr is the herd life of the animals(in years). Emissions were estimated for six groups ofanimals: suckler cow, replacement heifers age 41 year,fattened bulls age 41 year, fattened heifers age 41 year,heifer calves age o1 year and bull calves age o1 year.

Table 1Carcass weights for bulls, heifers and cows, total lifetime carcass yield per cow, aintensive basis – (BAS), roughage based – (RB) and minimum concentrates – (Mmedium¼5.87 MJ NE/kg DM, late¼5.18 MJ NE/kg DM) for the intensive (I) and

Abbrevation BAS RB

Early

I E I E

Carcass weight CAWBulls 365 278 300 2Heifers 275 237 274 2Cow 343 299 343 2

Total carcass yielda 1490 1363 1196 13

Age at first calving AFC 29 27 36

Profit (EUR) per suckler cow and year 1104 1006 1339 12

a Lifetime carcass yield per cow, includes suckler cow, bulls and surplus hei

The CH4 conversion rate (Ym) for suckler cows used informula 2 was calculated using the following formuladeveloped for dairy cows as no separate equation areavailable for suckler cows (Statistics Norway, 2008):

Ymcow ¼ 10:00�0:0002807nMilk

�0:02304nConcentrate_prop ð3Þ

where milk was milk yield, concentrate_prop wasproportion of concentrates (on net energy basis).

The concentrate proportion for suckler cows for theintensive and extensive breed group in the BAS situationwas 8.6 and 7.6%, respectively. In the RB and MC situations,the proportion of concentrates was 0%. As described by Åbyet al. (2012a) the average milk yield is 1600 and 1100 kg forthe intensive and extensive breed group, respectively.

Ym in formula 1 for bulls, surplus heifers and replace-ment heifers was calculated separately for two periods (1)from day 150 to 365 days of age (Ymo1 yr) and (2) from366 days to slaughter (Ym41 yr) (Statistics Norway, 2008).

Ymo1 yr ¼ 9:79�0:0187nCAWþ0:3155nSLA ð4Þ

Ym41 yr ¼ 9:64�0:0045nCAWþ0:074nSLA ð5Þ

where CAW is carcass weight in kg and SLA is slaughter agein months. Slaughter age for bulls and surplus heifers for allincluded situations were 14.8, 18, 15.8 and 18 months for theextensive and intensive breed group, respectively. For repla-cement heifers, CAW and SLA were replaced with 53 or 52%(assumed dressing %) of live weight at first calving for theintensive and extensive breed group, respectively, and age atfirst calving.

Carcass weights for bulls and surplus heifers for theintensive and extensive breed group in the BAS, RB earlycut, RB medium cut, RB late cut, MC early, MC medium andMC late cut situations are shown in Table 1. Due to seasonalcalving, minimum required weight at mating and differ-ences in growth rate between the included situations, ageat first calving differed (Åby et al., 2013). Age at first calvingfor the included situations is also shown in Table 1.

N, GE and Ym used for the included animal groups areshown in Table 2.

ge at first calving and profit (EUR) per suckler cow and year for the semi-C) situations using three roughage qualities (early¼6.56 MJ NE/kg DM,extensive (E) breed group.

MC

Medium Late Early Medium Late

I E I E I E I E I E

49 207 162 146 116 311 257 244 193 192 15659 237 220 205 183 274 259 237 220 205 18399 343 299 343 299 343 299 343 299 343 29953 977 907 828 768 1218 1394 1050 1139 917 853

24 36 36 36 36 36 24 36 26 36 36

50 1151 1017 1036 934 1282 1221 1140 1048 1025 922

fers.

Table 2Values for factors used for calculation of enteric methane and methane and nitrous dioxide from manure management for the semi-intensive basis – (BAS), roughage based – (RB) and minimum concentrates – (MC)situations using three roughage qualities (early¼6.56 MJ NE/kg DM, medium¼5.87 MJ NE/kg DM, late¼5.18 MJ NE/kg DM) for the intensive (I) and extensive (E) breed group.

Animal category Production conditions Roughage quality Enteric methane Methane manure N2O

Na GE, MJ/dayb Ym, %c M, kg/dayd VS %e B0f MCF, %g Nex, kg/yrh

I E I E I E I & E I & E I & E I & E I & E

Suckler cow BAS 1 1 267 177 9.35 9.52 40 9.2 0.18 8 55RB Early 1 1 226 162 9.55 9.69 40 9.2 0.18 8 55

Medium 1 1 243 166 9.55 9.69 40 9.2 0.18 8 55Late 1 1 263 180 9.55 9.69 40 9.2 0.18 8 55

MC Early 1 1 226 163 9.55 9.69 40 9.2 0.18 8 55Medium 1 1 243 174 9.55 9.69 40 9.2 0.18 8 55Late 1 1 263 180 9.55 9.69 40 9.2 0.18 8 55

Bullso1 yr/41 yr

BAS 2.22 2.58 126/184 97/132 7.94/9.17 9.26/9.49 15/35 9.2 0.21 8 24/35RB Early 1.96 2.16 60/135 57/132 9.16/9.46 9.80/9.61 15/35 9.2 0.21 8 24/35

Medium 1.96 2.16 47/106 42/93 10.91/9.88 11.42/10.00 15/35 9.2 0.21 8 24/35Late 1.96 2.16 37/71 32/71 12.04/10.15 12.29/10.21 15/35 9.2 0.21 8 24/35

MC Early 1.96 2.62 62/145 59/137 8.95/9.41 9.65/9.58 15/35 9.2 0.21 8 24/35Medium 1.96 2.56 51/116 46/105 10.22/9.71 10.85/9.87 15/35 9.2 0.21 8 24/35Late 1.96 2.16 46/96 40/88 11.19/9.95 11.55/10.03 15/35 9.2 0.21 8 24/35

Surplus heiferso1 yr/41 yr

BAS 1.22 1.58 96/106 72/84 10.33/9.73 11.03/9.90 15/30 9.2 0.21 8 29/35RB Early 0.96 1.16 59/143 56/140 10.34/9.74 10.62/9.81 15/30 9.2 0.21 8 29/35

Medium 0.96 1.16 50/116 45/109 11.04/9.91 11.35/9.98 15/30 9.2 0.21 8 29/35Late 0.96 1.16 39/95 37/86 11.63/10.05 11.99/10.13 15/30 9.2 0.21 8 29/35

MC Early 0.96 1.62 59/143 56/140 10.34/9.74 10.62/9.81 15/30 9.2 0.21 8 29/35Medium 0.96 1.56 50/116 45/109 11.04/9.91 11.35/9.98 15/30 9.2 0.21 8 29/35Late 0.96 1.16 39/95 37/86 11.63/10.05 11.99/10.13 15/30 9.2 0.21 8 29/35

Replacement heifersbirth to first calving

BAS 1 1 113 91 12.68/10.28 13.32/10.44 15/30 9.2 0.21 8 29/35RB Early 1 1 86 75 14.91/10.80 12.84/10.33 15/30 9.2 0.21 8 29/35

Medium 1 1 93 75 14.91/10.80 16.32/11.14 15/30 9.2 0.21 8 29/35Late 1 1 101 81 14.91/10.80 16.32/11.14 15/30 9.2 0.21 8 29/35

MC Early 1 1 84 79 14.91/10.80 12.53/10.25 15/30 9.2 0.21 8 29/35Medium 1 1 89 76 14.91/10.80 13.00/10.36 15/30 9.2 0.21 8 29/35Late 1 1 95 75 14.91/10.80 16.32/11.14 15/30 9.2 0.21 8 29/35

a number of animals;b gross energy intake;c methane conversion rate;d manure production per animal;e volatile solids;f maximum methane-producing capacity;g methane conversion factor;h nitrogen excretion.

B.A.Å

byet

al./Livestock

Science158

(2013)1–11

4

Table 3Proportion of manure management system (MS) for suckler cows, bulls,surplus and replacement heifers and emission factors (EF) for differentmanure management systems.

Manuremanagementsystem

MS

Sucklercow

Bulls, surplus heifers andreplacement heifers

EF N20-N/kg N

Liquid 0.50 0.64 0.001Solid storage anddrylot

0.10 0.05 0.02

Pasture rangeand paddock

0.40 0.31 0.02

B.A. Åby et al. / Livestock Science 158 (2013) 1–11 5

2.2.2. Methane from manure managementMethane from manure management was estimated

using an equation from Statistics Norway (2008)

Ei ¼NinMinVSinB0in0:67 kg=m3nMCFi ð6Þ

where Ei is the emission factor (in kg) for animal group i(i¼4; 1¼suckler cow, 2¼bulls, 3¼surplus heifers and4¼replacement heifer), Ni is the number if animals inanimal group i, Mi is the manure production per animaland lifetime (in kg) for animal group i, VS is volatile solids(kg degradable organic material/kg manure) for animalgroup i, B0i is maximum methane-producing capacity(m3/kg VS) for animal group i, MCF is methane conversionfactor (m3 CH4/m3) for animal group i, 0.67 is the conver-sion factor from m3 CH4 to kg CH4 (IPCC, 2001).

2.2.3. N2O from manure managementThe N2O emissions from manure management are

dependent on the Nitrogen excretion in each animalgroup, the proportion of manure managed in three differ-ent systems (liquid system, solid storage or drylot andpasture range and paddock), and the nitrous oxide emis-sion factor to each management system. Thus, the nitrousoxide from manure manamenent was estimated from theformula (Statistics Norway, 2008)

E¼∑s∑iððNinNexinMSiÞnEFisÞ ð7Þwhere Ni the number of animals in animal group i, Nexis the excretion of Nitrogen (kg N/animal and life), MSis fraction of manure managed in manure managementsystem s (s¼3; 1¼ liquid, 2¼solid/drylot or 3¼pasture/paddock), and EF is N2O emission factor (kg N20-N/kg N).

Values forM, VS, Bo, MCF, Nex, MS and EF for the animalgroups were obtained from Statistics Norway (2008) andBriseid et al. (2008) and are shown in Tables 2 and 3.

2.3. Effect of changed shadow price

According to Aune (1998), the shadow price is stabil from2010 to 2019 when it increases dramatically. In addition,another increase in the shadowprice is expected in 2027. Inthis study the sensitivity of economic values of two alter-native shadow prices were considered: 0.07 and 0.09 EURper kg CO2-equivalents, which corresponds to the expectedshadow prices in 2020 and 2030, respectively.

3. Results

3.1. GHG emissions

Table 4 shows the total GHG emissions in ton CO2-equivalents in addition to emissions from suckler cow, repla-cement heifer and fattened animals. The suckler cow was thelargest source of emissions accounting on average for 61 and62% for the intensive and extensive breed group, followed byreplacement heifers (20%) and fattened animals (19%) for theintensive breed group. For the extensive breed group fattenedanimals contributed 23% while replacement heifers accountedfor 16%. Of the included sources of GHG emission, entericfermentation contributed by far the most to total emissions(on average 83 and 81% for the intensive and extensive breed

group). Manure management contributed with 10 and 13% ofthe total GHG emissions for the intensive and extensive breedgroup, respectively. The proportions of nitrous oxide were 6%and 7%.

Total GHG emissions were highest for the intensivebreed group and for the BAS situation.

3.2. Economic parameters for GHG emission costs

The BAS situation gave the highest emission costs forboth breed groups (140 and 115 EUR per suckler cow andyear for the intensive and extensive breed group, respec-tively). The lowest emission costs were obtained for the RBearly cut roughage situation for the intensive breed group(113 EUR per suckler cow and year), while for the exten-sive breed group the lowest costs were found for the RBmedium cut roughage situation (95 EUR per suckler cowand year). Table 1 shows the obtained profit includingGHG emission costs for all production conditions. Thehighest profit was obtained for the RB early cut roughagesituation for both breed groups (1339 and 1250 EUR persuckler cow and year for the intensive and extensive breedgroup). In contrast, the lowest profit was aquired for theMC late cut roughage situation for both breed groups(1025 and 922 EUR per suckler cow and year). In general,the intensive breed group obtained the highest profit forall simulated production conditions. However, the differ-ences between the two breed groups were minor (lessthan 100 EUR per suckler cow and year for most of theincluded situations).

3.3. Economic values including GHG emission costs

Relative economic values in per cent are given in Table 5.When expressed as relative economic values, herd life of cowwas always the most important trait followed by carcassweight for the intensive breed group. For the extensive breedgroup carcass weight was more important than herd life ofcow in the basic and MC medium cut situations. Growth ratein the periods from weaning to 365 days and 365 daysto slaughter were the subsequent important traits in allincluded situations for both breed groups with the exceptionof RB and MC for the early cut roughage situation. For thesesituations, the fertility traits age at first calving and calvinginterval were more important. Birth weight, stillbirth, limband claw disorders and twinning had no real economic

Table 4Greenhouse gas emissions (ton CO2-equivalents per year) for suckler cow, replacement heifer and fattened animals of intensive and extensive breed groupfor the semi-intensive basis – (BAS), roughage based – (RB) and minimum concentrates – (MC) situations using three roughage qualities (early¼6.56 MJ NE/kgDM, medium¼5.87 MJ NE/kg DM, late¼5.18 MJ NE/kg DM).

Production conditions Greenhouse gas emissions (ton CO2-equivalents)

Intensive Extensive

Suckler cow Repl. heifer Fattened animals Total Suckler cow Repl. heifer Fattened animals Total

BAS 2.49 0.55 0.96 4.00 1.99 0.41 0.85 3.26RB early cut 1.91 0.67 0.66 3.24 1.91 0.34 0.83 3.07RB medium cut 2.03 0.71 0.58 3.32 1.67 0.55 0.58 2.73RB late cut 2.17 0.48 0.76 3.41 1.70 0.62 0.46 2.79MC early cut 1.91 0.67 0.67 3.25 1.94 0.34 0.85 3.13MC medium cut 2.03 0.59 0.70 3.32 2.00 0.36 0.70 3.06MC late cut 2.17 0.73 0.53 3.43 1.70 0.59 0.50 2.79

Table 5Relative economic values (in %) for the two breed groups (intensive and extensive) for the semi-intensive basis situation (BAS) and the roughage based (RB)and minimum concentrates (MC) based situations considering three roughage qualities (early=6.56 MJ NE/kg DM, medium=5.87 MJ NE/kg DM, late=5.18 MJNE/kg DM).

Trait Intensive Extensive

RB MC RB MC

BAS Early Medium Late Early Medium Late BAS Early Medium Late Early Medium Late

Herd life 35.41 39.82 37.95 31.48 38.84 36.21 32.04 25.05 32.15 32.75 27.70 30.90 28.68 29.30Age of first calving 5.10 7.05 5.14 2.97 7.83 6.35 4.67 3.38 4.91 3.43 1.84 5.68 4.26 3.49Calving interval 4.90 6.75 5.48 4.53 6.57 5.77 4.80 4.50 7.19 5.04 3.90 7.04 5.82 4.31Calving difficulty 2.39 2.51 2.84 3.08 2.46 2.77 2.91 2.78 2.75 2.96 2.93 2.69 3.00 2.94Stillbirth 0.03 0.07 0.04 0.02 0.07 0.05 0.03 0.02 0.06 0.03 0.01 0.06 0.03 0.02Twinning frequency 0.00 0.01 0.01 0.00 0.01 0.01 0.00 0.00 0.01 0.00 0.00 0.01 0.00 0.00Limb and claw 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.02 0.03 0.03SUM functional traits 47.86 56.24 51.49 42.11 55.81 51.19 44.50 35.75 47.09 44.24 36.41 46.41 41.83 40.09

Birth weight 0.09 0.18 0.23 0.26 0.19 0.23 0.25 0.21 0.34 0.39 0.40 0.35 0.42 0.40Growth rate:Day 0–200 4.43 2.87 3.38 3.77 2.89 3.38 3.70 6.57 4.40 4.86 4.96 4.42 5.13 5.04Day 200–365 6.92 3.70 6.19 11.55 4.00 5.97 9.65 9.73 5.40 8.61 14.50 5.79 8.86 11.21Day 365-slaughter 7.45 3.56 5.70 9.19 3.92 5.42 8.37 7.79 4.26 6.32 10.90 4.59 6.37 8.66Carcass weight 25.41 25.62 26.51 27.03 25.35 26.97 27.11 30.06 28.62 28.00 26.55 28.50 29.19 27.88Carcass conformation 5.40 5.17 4.46 4.28 5.15 4.66 4.61 5.22 4.98 3.83 3.25 5.02 4.17 3.50Fatness 2.44 2.65 2.06 1.80 2.69 2.18 1.81 4.67 4.91 3.75 3.02 4.92 4.03 3.22SUM production traits 52.14 43.76 48.51 57.89 44.19 48.81 55.50 67.62 52.91 55.76 63.59 53.59 58.17 59.91

B.A. Åby et al. / Livestock Science 158 (2013) 1–116

importance in any of the simulated situations. The rest of theincluded traits were of moderate economic importance, andsome minor reranking of the traits between the includedsituations was observed.

For the intensive breed group, functional traits wereequally important as production traits ranging between42% for the RB late cut roughage situation and 56% for theRB early cut roughage. For the extensive breed group,the importance of functional traits was lower than for theintensive breed group and vice versa for the productiontraits. For both breed groups, the importance of functionaltraits was highest for the RB early cut roughage (56 and47% for the intensive and extensive breed group), withdecreasing importance as roughage quality was reduced.In general, there were only minor differences in relativeeconomic values between the RB and MC situations forboth breed groups and only minor reranking of the traitswere observed.

Fig. 1 show the effects of including GHG emission costsinto the calculations of economic values for the BAS and RBmedium cut roughage situations. The effects of including GHGemissions are given as deviations in percentage points fromthe relative economic values presented in Åby et al. (2012a)for the BAS situation and Åby et al. (2013) for the RB mediumcut roughage. The other simulated situations showed thesame trend as the RB medium cut roughage situation, mean-ing the effect of including emission costs were higher forintensive breed group. The inclusion of emission costs reducedthe relative importance of herd life of cow, age at first calving,stillbirth, twinning, birth weight and the sum of the functionaltraits. All other traits showed increased economic importancewith the inclusion of emission costs. Even so, as can be seenfrom Fig. 1, the effect of including GHG emission costs into thecalculation of economic values was minor. However, the effectof including emission costs increased as roughage quality wasreduced. The deviation for the sum of functional traits for the

B.A. Åby et al. / Livestock Science 158 (2013) 1–11 7

intensive breed group ranged from �6.97 percentage pointsfor the RB late cut roughage to �1.23 percentage points forthe MC early cut roughage. For the extensive breed group thedeviation ranged from �4.74 percentage points for the RB latecut roughage to �2.73 percentage points for the MC early cutroughage.

3.4. Effect of changed shadow price

The effect of increased shadow price on the relativeimportance of production traits for the BAS and RB early,medium and late cut roughage situations is shown in Fig. 2.

-5.00 -4.00 -3.00 -2.00 -1.00

%

-5.00 -4.00 -3.00 -2.00 -1.00 0.00

% poin

Effect of including emission c

Herd life

Age at first calving

Calving intervalStillbirth

TwinningCalving difficulty

Limb and claw disorders

SUM functional traits

Birth weight

Growth rate 0-200 days

Growth rate 200-365 days

Growth rate 365 days-slaughterSlaughter weight

Carcass conformation

Fatness

SUM production traits

Effect of including emission co

Herd life

Age at first calvingCalving interval

Stillbirth

TwinningCalving difficulty

Limb and claw disorders

SUM functional traits

Birth weight

Growth rate 0-200 days

Growth rate 200-365 days

Growth rate 365-slaughter

Slaughter weight

Carcass conformation

FatnessSUM production traits

Fig. 1. Effect of including emission cost into calculation of economic values aintensive and extensive breed groups and two production conditions; semi-inteNE/kg DM) (b).

The trend was similar for the MC situations. As the shadowprice increased, the production traits became more impor-tant, and thus the relative importance of functional traitsdecreased. The largest effect of increased shadow pricebetween the highest and lowest shadow price was observedfor the RB late cut roughage for both breed groups (7.74 and6.23 percentage points increase in the sum of productontraits for the intensive and extensive breedgroup, respec-tively). Likewise, the smallest effect was found for the RBearly cut roughage (4.20 and 4.73 percantage points for theintensive and extensive breed group). These results showsthat large increases in shadow prices have only moderate

0.00 1.00 2.00 3.00 4.00 5.00

point

1.00 2.00 3,00 4,00 5,00

t

osts into EV for BAS situation

Intensive BAS

Extensive BAS

sts into EV for the Medium cut roughage

Intensive Medium cut

Extensive medium cut

s deviations from relative economic values without emission costs fornsive BAS situation (a) and roughage based medium cut roughage (5.87 MJ

Intensive breed group Extensive breed group

40.0045.0050.0055.0060.0065.0070.00

% p

rodu

ctio

n tr

aits

EarlyMedium

BasicLate

Production conditions

3.5

7.0

8.7

3.5

7.0

8.7

45.0050.00

55.00

60.00

65.00

70.00

% p

rodu

ctio

n tr

aits

EarlyMedium

LateBasicProduction conditions sh

adow

pric

e, E

uro

cent

s/to

n C

O2e

q

shad

owpr

ice,

Eur

o ce

nts/

ton

CO

2eq

Fig. 2. Effect of changed shadow price on the relative economic importance of production traits for the intensive and extensive breed group for the semiintensive – (BAS), roughage based (RB) situations including three roughage qualities (early cut¼6.56 MJ NE/kg DM, medium cut¼5.87 MJ NE/kg DM, latecut¼5.18 MJ NE/kg DM).

B.A. Åby et al. / Livestock Science 158 (2013) 1–118

effects on the relative economic values, indicating robusteeconomic values to changes in shadow price. Even so, insome of the included situations the economic values forstillbirth and twinning frequency changed sign when theshadow price increased, indicating that improving thesetraits is no longer profitable. For the intensive breed groupthis was so for the RB late cut roughage and shadow price0.09 EUR per kg CO2-equivalents. For the extensive breedgroup both the RB and MC late cut roughage situations andshadow prices of 0.07 and 0.09 EUR per kg CO2-equivalentsled the a change in sign of the economic values of thesetraits.

4. Discussion

In the subsequent paragraphs, only factors related tothe estimation of GHG emissions and shadow price sensi-tivity are included. Discussions related to other parametersused in the deterministic model and additional sensitivityanalyses are given by Åby et al. (2012a) and Åby et al.(2013).

4.1. GHG emissions

The approach used to calculate GHG emissions in thepresent study is mainly the same as in the National Inventoryreport submitted by Norway to the United Nations Frame-work Convention on Climate Change (UNFCCC). The calcu-lated methane emission factors for cattle used in this study(Table 1) is considerable higher than the IPCC Tier 2 defaultvalue of 6.5% (IPCC, 2006), which is used as a basis in otherstudies on GHG emissions from dairy and beef production(e.g., Beauchemin et al., 2011; Bonesmo et al., 2013; Casey andHolden, 2006). According to Statistics Norway (2008) thisdiscrepancy may be explained by several factors: (1) dietcomposition (higher forage proportion in Norway), (2) mod-erate milk yield (average 7300 kg in 2011) and (3) thescientific approaches used to predict CH4 from enteric fer-mentation. The approach used to calculate emission factorsfor bulls, surplus and replacement heifers (Eqs. (5) and (6))indicate that the emission factor increase as when slaughterage increases (constant carcass weight), and reduced whencarcass weight increases (constant slaughter age). This indi-cate higher emission factors on extensive diets (roughagebased), and lower for emissions factors for intensive diets(more concentrates), respectively. However, some of the

situations included in our study are far from the currentnormal Norwegian practice (average carcass weight of 295 kg)(Ruud et al., 2013), meaning that this approach may not besuitable for the scope of our study. Bonesmo et al. (2013) usedthe IPCC (2006) emission factor of 6.5%, adjusted for thedigestibility of dry matter (DM) in their study of GHGemissions from Norwegian dairy and beef production. For aroughage with a digestibility of 45% (lower than the late cutroughage used in the present study), the emission factor is 8%.It therefore seems that using the Norwegian Emission Inven-tory methodology to estimate methane emission factors maylead to overestimation of the GHG emissions. However, therobustness of the economic values towards change in emis-sion costs as found in the present study suggest that usinganother approach (e.g., Bonesmo et al., 2013) to estimate GHGemissions would only have minor effect on the resultsobtained in our study.

In the emission inventory, averages for manure andnitrogen excretion are used, not taking breed differencesinto account (Table 2). As suckler cows of the intensivebreed group have a higher feed intake, one may alsoassume that the manure production and nitrogen excre-tion are higher. This means that the GHG emissions for theintensive breed group may be somewhat understimated.A project aiming to improve the estimates of manure andnitrogen excretion under Norwegian production condi-tions was initiated in 2011 (Nesheim et al., 2011). Evenso, the calculated contributions from manure and nitrousoxide emissions in our study were small and improvedestimates for manure production and nitrogen excretionshould therefore not have a large impact on the resultsobtained.

The approach used in this study included emissions fromenteric fermentation and manure management, only. Thismakes direct comparisions with other studies using life cycleassessment (LCA) impossible because these studies (e.g.,Beauchemin et al., 2010, 2011; Casey and Holden, 2006)additionally include emission factors contributing to carbondioxide emissions such as the production of fertilizer, use ofmachinery, electricity and emission of N2O from soil. Inaddition, as pointed out by Beauchemin et al. (2010), thestudies differ in farming systems, assumptions and appro-aches to calculate emissions which make comparisons ofstudies difficult. Regardless, according to Beauchemin et al.(2010), the emissions of CO2 and soil N2O contributed only 9%of the total GHG emissions only, indicating that including

B.A. Åby et al. / Livestock Science 158 (2013) 1–11 9

these sources of GHG emissions would have had very minoreffects on the results of our study.

In the present study, the suckler cow contributed themost to the emission (on average 61% and 62% for theintensive and extensive breed group). This is somewhatlow compared to Beauchemin et al. (2010), who found thatsuckler cows contributed to 79% of the total emissions. Thesingle most important souce of emission in the presentstudy was by far enteric fermentation contributing onaverage 84% and 81% for the intensive and extensive breedgroup. Casey and Holden (2006) found that enteric fer-mentation contributed 49–60% in comparable productionconditions to this study, however including more sourcesof emission than is this study (enteric, fertilizer, dungmanagement, supplements fed, diesel and electricity).

The environmental effect is not the only problem asso-ciated with methane emission. As pointed out by Beaucheminet al. (2008) methane emissions from ruminants is essentialwaste of energy. In a study by Hegarty et al. (2007) 4.9% of theGE intake was lost as methane. Therefore, a reduction in theemissions of methane is expected to result in a correspondingimprovement in feed efficiency or vice versa. Cattle selectedfor low residual feed intake (RFI, defined as the differencebetween the measured feed intake and the expected feedintake, based on requirements for maintenance and growth)have a lower methane emission than the cattle selected forhigh RFI (Hegarty et al., 2007). Reducing RFI may also havebeneficial effects on nitrous oxide emissions from manure,due to a reduced intake of dietary nitrogen (Alford et al.,2006). As RFI is very expensive and time consuming tomeasure, methane emission may be of interest as an indicatortrait, given that quick, reliable and inexpensive measure ofmethane emission becomes available. The direct measure-ment of methane emissions may influence to the economicvalue of feed efficiency. The economic value of feed efficiencymay be calculated as saved feed costs on constant animalperformance. The inclusion of methane emissions willincrease the economic value of this trait, as improving feedefficiency will reduce emission costs, in addition to feed costs.

4.2. GHG emission costs

Including GHG costs into the profit function considered atfarm level as done in our study implies that the emissioncosts are to be paid by the farmer. Although it is logical thatthe emission costs are to be paid by the polluter (i.e., thefarmer), and therefore should be included into the calcula-tions of economic values, this costs may also be considered atother levels e.g., national. One may argue that it is incorrectthat farmer should bear the emission costs as the productionof food is a necessity and the society benefits from thereduced emissions resulting from improved animal produc-tivity. On the other hand, farmers may pass the cost on toconsumers by increasing the price per unit of product. Evenso, the effect of including emission costs on the profit wasmoderate, reducing the profit per suckler cow and year byapproximately 10%.

The idea of taxes on GHG emissions is nothing new. Forexample, taxes on Petrol and Diesel is implemented in manycountries worldwide (OECD, 2007). Similarly, taxes havebeen proposed for emissions from ruminants for example

in New Zealand, Denmark, USA and Ireland. However, theproposals were strongly opposed, and not implemented sofar (Leip et al., 2013; Lennox et al., 2007). Therefore, thepossibilities of a real tax on methane emissions from cattleseem unlikely in the foreseeable future.

4.3. Economic values including GHG emission costs

The mechanisms influencing the income and costs andthus the economic values for all traits are discussed indetail in Åby et al. (2013). Therefore, only the details onhow the economic values are influenced by the inclusionof total emission costs are highlighted here. Prolonging theherd life of cow increased the emission cost due to highertotal gross energy intake (both of suckler cow and pro-geny). When prolonging age at first calving, the emissioncosts were reduced. This was due to the fact that thehigher emission due to increased total feed consumptionof the replacement heifer was counteracted by the largerreductions in gross intake by the suckler cow. Extendingthe calving interval meant that fewer calves were born,hence reducing the emissions from fattening. In addition, alarger proportion of cows were kept barren throughwinter as described by Åby et al. (2012a). Due to this fact,the total emissions were increased. A higher incidence ofstillbirth meant fewer animals for fattening, and thereforelowered emission costs. For twinning frequency the samemechanism meant increased emissions due to flereanimals for fattening. Increased birth weight resulted inlarger emission cost due to higher feed requirements forgestation and lower emission cost due to reduced feedrequiremens for fattening (fewer days of feed). The neteffect of this was an increase in emission cost due tohigher feed requirements for gestation. Calving difficulty,limb and claw disorders, carcass conformation and fatnessdid not influence GHG emissions; therefore the economicvalues of these traits were independent of the inclusion ofemission cost into the calculations. Increasing growth ratein any one of the including three periods and birth weightreduced the emission cost by decreasing the length of thefattening period and thus the total gross energy intake.

It is important to consider the effect of including the GHGemission costs into the estimation of economic values. As canbe seen from Fig. 1, the economic importance of herd life ofcow, age at first calving, stillbirth, twinning and the sum ofthe functional traits were decreased when emission costswere included in the calculations. For herd life of this wasdue to the fact that the additional emission costs reduced theextra profit gained in improvements of this trait. For stillbirthand age at first calving the emission costs were reduced dueto fewer fattened animals and decreased gross energy intakeof the suckler cow (only for age at first calving), meaning thatthe marginal economic importance was decreased. In con-trast, the relative importance of calving interval increasedwhen GHG emission costs was included. This is a mainly afunction of increased GHG emission costs when calvinginterval are prolonged, due to a larger proportion of barrencows. Even so, of all the functional traits, herd life of cow wasthe most influenced by including GHG emission costs. Therelative importance of growth rate in all three includedperiods increased when emission costs were included.

B.A. Åby et al. / Livestock Science 158 (2013) 1–1110

Increasing growth rate in any of the three periods will giveextra costs reductions by lowering the emission costs byreducing the fattening period and thus the total gross energyintake. The economic values of carcass weight, carcassconformation and fatness, limb and claw disorders andcalving difficulty were not influenced by emission costs.The increased economic importance of these traits wastherefore a result of the reduced economic importance ofthe functional traits. Even so, the effects of including emis-sion costs into the calculation of economic values were smallas can be seen from Fig. 1. In addition, very little reranking oftraits was observed. This suggests that the relative economicvalues are robuste to the inclusion of emission costs into thecalculation of economic values.

Including GHG emission costs into the calculation ofeconomic values increased as roughage quality wasreduced. This was due to the fact that the change inemission costs for the included traits were similar for allthe simulated situations. The economic values of thefunctional traits, especially herd life of cow, were reducedwith decreasing roughage quality as shown in Åby et al.(2013), meaning that the impact of including emissioncosts increased as roughage quality was reduced.

The effect of including GHG emissions was larger forthe intensive breed group for all the included situationswith the exception of the BAS situation. This was becausethe change in emission costs was higher for the intensivebreed group due to higher gross feed intake of all animalcategories (Table 2). By contrast, the effect of includingGHG emissions costs was somewhat larger for the exten-sive breed group in the BAS situation. Even so, the changein emission costs was still higher for the intensive breedgroup. However, the BAS situation had the largest differ-ence the economic values of herd life of cow for the twobreed groups (meaning emission costs accounted for alarger proportion of the economic values for the extensivebreed group).

4.4. Sensitivity of shadow prices

The sensitivity analysis covered the expected shadowprices from 2010 to 2030. This indicated a 250% increase inthe price per kg of CO2-equivalents. Despite this huge rangein prices, the sensitivity analysis showed that the effect ofchanged shadow price were only minor. This indicates thatthe relative economic values are robust to changes in shadowprices. Even so, the combinations of late cut roughage andhigh shadow price led to a change in sign of the economicvalues of stillbirth and twinning frequency, indicating thatgenetic improvement of these traits are no longer profitable.Under such conditions, keeping suckler cows for beef pro-duction and operating a breeding scheme is not sustainableand a rescaling of the enterprice must occur.

5. Conclusions

Including GHG emissions into calculation of economicvalues decreased the relative economic importance of thefunctional traits herd life, age at first calving, stillbirth andtwinning incidence while increasing the importance of theproduction traits. However, the overall effect of including

GHG emission was small and little reranking between thetraits was observed. The sensitivity analysis investigatingthe effect of shadow price showed higher importance ofproduction traits as shadow price increased, however thiseffect was only minor. The small effect of including GHGemission into the calculation of economic values andchanged shadow price demonstrate the robustness of therelative economic values. Thus, breeding goals for beefcattle considering both production and functional traits donot have to be changed considerably in a situation whereemission of GHG are to be taken into account.

Conflict of interest statement

I hereby confirm that the authors have no conflicts ofinterest concerning the paper “Effect of incorporatinggreenhouse gas emission costs into economic values oftraits for intensive and extensive beef cattle breeds”.

Acknowledgements

This project was financed by the Research Council ofNorway, Geno Breeding and AI Association, The NorwegianBeefbreeders Association, Animalia Meat and Poultry ResearchCentre, Nortura SA, the Norwegian Insitute of Food, Fisheriesand Aquaculture Research: Grant no. 186898 (Improvement ofbeef quality and increased potential for Norwegian beefproduction). The authors thank Odd Magne Harstad forvaluable input on the methodology on estimation of GHGemissions.

References

Alford, A.R., Hegarty, R.S., Parnell, P.F., Cacho, O.J., Herd, R.M., Griffith, G.R.,2006. The impact of breeding to reduce residual feed intake onenteric methane emissions from the Australian beef industry.Australian Journal of Experimental Agriculture 46, 813–820.

Aune, R., Bye, T., Hansen, M.I. & Johnsen, T.A., 1998. Gas price and shadowprice for CO2-emission in Norway until 2027. (In Norwegian) Avail-able at: ⟨http://www.ssb.no/histstat/not/not_9854.pdf⟩.

Beauchemin, K.A., Kreuzer, M., O'Mara, F.O., McAllister, T.A., 2008.Nutritional management for enteric methane abatement: a review.Australian Journal of Experimental Agriculture 48, 21–27.

Beauchemin, K.A., Janzen, H.H., Little, S.M., McAllister, T.A., McGinn, S.M.,2010. Life cycle assessment of greenhouse gas emissions from beefproduction in western Canada: A case study. Agricultural Systems103, 371–379.

Beauchemin, K.A., Janzen, H.H., Little, S.M., McAllister, T.A., McGinn, S.M.,2011. Mitigation of greenhouse gas emission from beef production inwestern Canada – evaluation using farm-based life cycle assessment.Animal Feed Science and Technology 166–167, 663–677.

Briseid, T., Grønlund, A., Harstad, O.M., Garmo, T., Volden, H. & Morken, J.,2008. Greenhouse gases from agriculture (In Norwegian) Bioforsk.Available at: ⟨https://www.slf.dep.no/no/miljo-og-okologisk/klima/klimaprosjekter/utslipp/_attachment/13419?_ts=12e6bb311c8⟩(accessed: 08.01.12).

Bonesmo, H., Beauchemin, K.A., Harstad, O.M., Skjelvåg, A.O., 2013.Greenhouse gas emission intensities of grass silage based dairy andbeef production: a system analysis of Norwegian farms. LivestockScience 152, 239–252.

Casey, J.W., Holden, N.M., 2006. Quantification of GHG emissions fromsuckler beef production in Ireland. Agricultural Systems 90, 79–98.

Cottle, D.J., Nolan, J.V., Wiedemann, S.G., 2011. Ruminant enteric methanemitigation: a review. Animal Production Science 51, 491–514.

DEFRA, 2007. The Social Cost of Carbon: What They Are, And How To UseThem in Economic Appraisal in the UK. Available at: ⟨http://www.decc.gov.uk/assets/decc/what%20we%20do/a%20low%20carbon%20uk/carbon%20valuation/shadow_price/background.pdf⟩ (accessed: 07.01.12).

B.A. Åby et al. / Livestock Science 158 (2013) 1–11 11

FAO, 2006. Livestock's Long Shadow: Environmental Issues and Options.Food and Agriculture Organization. Rome, Italy, pp. 416.

Garnsworthy, P.C., 2004. The environmental impact of fertility in dairycows: a modelling approach to predict methane and ammoniaemissions. Animal Feed Science and Technology 112, 211–223.

Hegarty, R.S., Goopy, J.P., Herd, R.M., McCorkell, B., 2007. Cattle Selectedfor Lower Residual Feed Intake have Reduced Daily Methane Produc-tion. 85, pp. 1479–1486.

IPCC, 2001. Good Practice Guidance and Uncertainty Management in NationalGreenhouse Gas Inventories, IPCC. Available at: ⟨http://www.ipcc-nggip.iges.or.jp/public/gp/english/4_Agriculture.pdf⟩ (accessed: 08.01.12).

IPCC, 2006. Guidelines for national greenhouse gas inventories. In:Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. (Eds.),Prepared by the National Greenhouse Gas Inventories Programme,IGES, Japan.

IPCC, 2007. IPCC fourth assessment report: climate change 2007. Con-tribution of Working Group I to the Fourth Assessment Report of theIntergovernmental Panel on Climate Change. Cambridge UniversityPress, Cambridge, United Kingdom and New York, USA. pp. 996.

Lassen, J., Løvendahl, P., Madsen, J., 2012. Accuracy of noninvasive breathmethane measurements using Fourier transform infrared methods onindividual cows. Journal of Dairy Science 95 (2), 890–898.

Leip, A., Weiss, F., Wassenaar, T., Perez, I., Fellmann, T., Loudjani, P.,Tubiello, F., Grandgirard, D., Monni & S., Biala, K., 2010. Evaluation ofthe Livestock Sector's Contribution to the EU Greenhouse Gas Emis-sions (GGELS) – Final Report European Commission Joint ResearchCentre. ⟨http://ec.europa.eu/agriculture/analysis/external/livestock-gas/full_text_en.pdf⟩. (accessed: 25.02.13).

Lennox, J.A., Andrew, R. & V. Forgie, 2007. Price effects of an emissionstrading scheme in New Zealand. In: Proceedings of the 107th EAAESeminar, Seville, Spain, January 29th–February 1st.

Nesheim, L., Dønnem, I. & Daugstad, K., 2011. Excretion of manure fromlivestock-assessment of standards (In Norwegian). Bioforsk Report Nr.74 Bioforsk. pp. 19.

OECD, 2007. The Political Economy of Environmentally Related Taxes.⟨http://www.oecd.org/environment/38046899.pdf⟩. (accessed 26.02.13).

Ogino, A., Hideki, O., Shimada, K., Hirooka, H., 2007. Evaluating environ-mental impacts of the Japanese beef cow–calf system by the life cycleassessment method. Animal Science Journal 78, 424–432.

Phocas, F., Bloch, C., Chapelle, P., Bècherel, F., Renand, G., Mènissier, F.,1998. Developing a breeding objective for a French purebred beefcattle selection programme. Livestock Production Science 57, 49–65.

Ruud, T.A., Wittussen, H.T., Juul-Hansen, B.O., Mellby, J.O., Røhnebæk, E.,Aass, L., Rustad, L.J., Anderssen, Å.M.F. & Nastad, O., 2013.Økt storfekjøttproduksjon i Norge- rapport fra ekspertgruppen,februar 2013 (In Norwegian). Available at: ⟨http://www.regjeringen.no/upload/LMD/Vedlegg/Brosjyrer_veiledere_rapporter/Kjoettgruppens_rapport_feb_2013.pdf⟩ (accessed: 13.03.13).

Statistics Norway, 2008. The Norwegian Emission Inventory, 2008.Documentation of methodologies for estimating emissons of green-house gases and long-range transboundary air pollutants. Oslo-Kongsvinger, Norway. Statistics Norway, pp. 252.

Statistics Norway, 2010. Available at: ⟨http://www.ssb.no/kpi/kpi.cgi?lang=n&beloep=200&framaaned=Gj.snitt&fraaar=1995&tilmaaned=Gj.snitt&tilaar=2010⟩ (accessed: 07.01.12).

UNFCCC, 2011. National Intentory Submissions 2011. Available at: ⟨http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/5888.php⟩ (accessed: 07.01.12).

Wall, E., Simm, G., Moran, D., 2010. Developing breeding schemes to assistmitigation of greenhouse gas emissions. Animal 4 (3), 366–376.

Wolfova, M., Wolf, J., Zahràdkovà, R., Pribyl, J., Dano, J., Krupa, E., Kica, J.,2005. Breeding objectives for beef cattle used in different productionsystems 2. Model application to production systems with the Char-olais breed. Livestock Production Science 95, 217–230.

Åby, B.A., Aass, L., Sehested, E., Vangen, O., 2012a. A bio-economic modelfor calculating economic values of traits for intensive and extensivebeef cattle breeds. Livestock Science 143 (2–3), 259–269.

Åby, B. A., Aass, L., Sehested, E., Vangen O., 2013. Effects of changes inexternal production conditions on economic values of traitsin Continental and British Beef cattle breeds. Livestock Science 150(1-3), 80–93.