Breeding objectives for sheep in Ireland: A bio-economic approach

Livestock Science 132 (2010) 135–144

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Livestock Science j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l i v s c i

Breeding objectives for sheep in Ireland: A bio-economic approach T.J. Byrne a,b,⁎, P.R. Amer a, P.F. Fennessy a, A.R. Cromie c, T.W.J. Keady d, J.P. Hanrahan d, M.P. McHugh e, B.W. Wickham c a b c d e

AbacusBio Limited, P O Box 5585, Dunedin, New Zealand Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand Irish Cattle Breeding Federation Society Limited, Highfield House, Shinagh, Bandon, Co. Cork, Ireland Teagasc, Athenry Research Centre, Athenry, Co. Galway, Ireland Teagasc, Ballyhaise, Co. Cavan, Ireland

a r t i c l e

i n f o

Article history: Received 4 March 2010 Received in revised form 23 May 2010 Accepted 24 May 2010 Keywords: Sheep Breeding objective Economic values Economic weights Bio-economic

a b s t r a c t Breeding objectives for meat sheep in Ireland have been defined and used in the development of selection sub-indices to provide commercial producers with an economic comparison of animals for specific performance trait groups. Using trait-by-trait bio-economic models and a range of methodologies, economic weights (in € per lamb born per genetic standard deviation in the trait) have been calculated for maternal and terminal sire performance traits as follows: production traits; −€1.41 for days to slaughter, €0.35 for carcase conformation class, −€0.52 for carcase fat class, lambing traits; −€0.69 for lambing difficulty for single-bearing ewes, −€0.37 for lambing difficulty for multiple-bearing ewes, €1.15 for lamb survival, maternal traits; €0.83 for number of lambs born, −€1.49 for ewe mature weight, health traits; −€0.09 for lamb foot rot, and −€0.82 for ewe foot rot. Results indicate the significant value of improving the ability of lambs to survive to weaning, without increasing number of lambs born. The highly negative economic weight for both days to slaughter and mature size represents a powerful unfavourable relationship between the two traits. Economic values for lambing difficulty and foot rot represent the significant costs these traits have in the Irish sheep production system. In the early stages of the development of the genetic improvement program for sheep in Ireland the breeding objective defined in this paper provides directed emphasis for trait recording, selection strategies, and mating systems. In addition the economic weights provide indications as to how much genetic improvement in a specific trait would be worth paying for. The proposed formulation of the total economic index as sub-indices provides the ability for commercial farmers to adjust breeding emphasis towards specific market outcomes or address key production aspects in their particular farming system. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Clearly defined breeding objectives not only simplify selection decisions but are also essential in the development of breeding strategies that define accurate selection criteria

⁎ Corresponding author. Tel.: + 64 3 4776375; fax: + 64 3 4776376. E-mail address: [email protected] (T.J. Byrne). 1871-1413/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.livsci.2010.05.013

for the relevant traits affecting commercial farm profitability. Economic values in multiple-trait selection indices provide a measurement of the economic importance of performance traits and guide the selection emphasis, implied by the overall breeding objective, proportional to the economic importance of the traits (Hazel, 1943). In recent years only a very small proportion of sheep flocks in Ireland have been involved in performance recording (Murphy et al., 1999), with the participating breeders recording only a small number of traits expressed by

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slaughter lambs. A new across breed genetic evaluation system that integrates commercial and pedigree sheep breeder data is under development, with inclusion of a wide range of economically relevant goal traits (Byrne et al., 2009). This development of multi-trait evaluation requires definition of the economic relativity of goal traits within a defined production system (Ponzoni, 1986). The work reported in this paper has defined economic weights for terminal and maternal traits in the breeding objective for the Irish sheep industry; the paper also reports developments in the definition of trait group sub-indices. 2. Materials and methods

Table 1 Feeding regimes and dietary components for ewes, replacement ewes, and lambs in each feeding period.

Ewes

Replacements

Lambs

Grass Silage Concentrate Grass Silage Concentrate Milk Grass Silage Concentrate

Spring (%)

Summer/ autumn (%)

Winter (%)

100 0 0 100 0 0 70 30 0 0

100 0 0 100 0 0 0 100 0 0

0 100 0 0 100 0 0 0 100 0

2.1. Breeding perspectives The economic implications of selective breeding were defined based on their impact on commercial farm profit. Price input variables, and revenue streams were calculated accordingly. The commercial production system model assumed spring lambing, with lambs being finished outdoors on grass to a carcase weight of 20 kg. Separate models developed in Excel (Microsoft Corporation 2007) and Mathcad 14 (Parametric Technology Corporation 2007) were used for different traits or trait groups as advocated by Amer (2006). It was implicitly assumed in all models that each trait change would not change the number of breeding ewes run on a commercial farm. 2.2. Sub-index structure Economic values are calculated as the economic effect on profit per unit change in each of the traits independently. Discounted genetic expression (DGE) coefficients account for the differential rates and timing of expressions for the different traits in the progeny of the male selection candidate. DGE coefficients therefore enable the economic values to be included in the breeding objective as economic weights which quantify the aggregate contribution to farm profit from expressions of the sires' genes per lamb born. As well as an overall ‘sheep value’ index, sub-indices for production, maternal, lambing and health have been included in order to provide index users with information on how overall merit for groups of traits affects the ranking of selection candidates. This is useful for commercial producers requiring an economic comparison of animals for specific performance trait groups, as has been successful in cattle breeding in Ireland (Amer et al., 2001). This may also be of value to breeders targeting different types of ram buyer, other than those specifically looking at average overall index. This type of structure is particularly relevant when considering differences in levels of production in small flocks and provides a compromise between a single industry breeding objective and customised breeding objectives for individual breeders (Amer et al., 2001). 2.3. Feed periods and the cost of energy The cost-of-energy model for the Irish farming system assumed that the calendar year can be divided into three defined periods of time each with a specified feeding regime. These feed periods represented spring, combined summer

and autumn, and winter with lengths of 100, 165, and 100 days respectively. Table 1 presents the feeding regimes for ewes, replacement ewes, and lambs in each feeding period. Dietary component costs were calculated on a per megajoule of metabolisable energy (MJME) basis. Assumed feed costs were €0.12 per kg dry matter (DM) consumed for grass, and €0.146 per kg DM consumed for baled and wrapped silage (Keady, 2009). The average cost of concentrate was €0.25 per kg DM consumed. Where applicable, the opportunity cost of available pasture was included, and valued at €0.15 per kg DM when fed to finishing lambs, assuming 200 MJME of feed energy fed as pasture increases lamb carcase weight by 1 kg (Nicol and Brookes, 2007). This extra carcase weight would be worth the average price per kilogram of €3.28 in summer and autumn (BordBia, 2008). Thus, the opportunity cost of pasture is based on either the foregone purchase of additional lambs for finishing, or alternatively the forgone retention of existing lambs. The cost of milk at €0.075 per kg DM was calculated taking into account the increase in pasture utilisation by the ewe (0.8 versus 0.4 by the lamb) and also the loss of energy in the ewe converting this pasture energy to milk (Freer et al., 2007). 2.4. Economic value of days to slaughter The economic value of days to slaughter was calculated at a constant slaughter live weight assuming that farmers will slaughter animals earlier if they grow faster. Components contributing to the value of days to slaughter included both the potential loss of seasonal premiums associated with an increase in days to slaughter and increased feed costs associated with longer time grazing until slaughter. Seasonal premiums per kilogram of carcase weight per day were calculated by moving the industry slaughter volume forward over the period of the year where premiums can be obtained for slaughtering animals earlier. Fig. 1 presents the average (over 12 years from 1997 to 2008 inclusive) price per kilogram of carcase weight throughout the year and defines the period of the year where seasonal premiums can be captured through increasing the growth rate of lambs. Feed costs per day were calculated from daily energy requirements for growing animals to a mature weight of 72 kg (Nicol and Brookes, 2007). The feed for finishing lambs was valued at the opportunity cost of available pasture.

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Fig. 1. Average weekly price/kg carcase weight of prime lamb (€ cents). The period for which seasonal premium can be captured is between weeks 21 and 31 inclusive.

2.5. Economic value of carcase quality traits Premiums and penalties for conformation and fat were calculated independently. The premiums and penalties for fat class were calculated at a constant conformation score and carcase weight, and those for conformation score were calculated at a constant fat class and carcase weight. The economic value of carcase conformation was derived using the existing carcase grading thresholds in the EUROP classification system and price differentials based on a proposed quality-based payment system put forward by Meat Industry Ireland (Anonymous, 2007). Proposed price differentials for these standard European Union (EUROP) carcase classes are shown in Table 2. Under the EUROP carcase classification scheme each carcase is assessed and classified at the weighing point in the slaughter process into one of five conformation classes which describe the development of carcase profiles. The letters represent an incremental scale ranging from E (the best conformation) through to P (the worst conformation). Each class on the five-class scale was assumed to incorporate three on the 15-point scale (Jones et al., 2004), to ensure consistency with the national genetic evaluation scale for the sheep carcase conformation trait in Ireland. The population average and the standard deviation were defined as 8 and 1.3 respectively giving a normal distribution of animal conformation scores on the 15-point scale. These scores were partitioned into the five classes, using appropriTable 2 Priced differential payments per kilogram of carcase weight above/below a base price of €3.48 for European Union carcase conformation classes from E (the best conformation) through to P (the worst conformation), and fat class classification from 1 (the least fat carcase) to 5 (the fattest carcase) for the quality-based payment system put forward by Meat Industry Ireland. Conformation class

Fat class

1 2 3 4 5

E

U

R

O

P

30 30 30 − 10 − 30

20 20 20 − 10 − 30

0 0 0 − 10 − 30

0 0 0 − 30 − 30

0 0 0 − 30 − 30

ate thresholds calculated from industry data on 1.9 million lamb carcases slaughtered in 2007. The economic value of an increase in fat percentage was calculated using a phenotypic regression of fat percent on carcase weight. Carcases were classified into five fat classes, from 1 to 5, in which 1 denotes the least fat carcase and 5 the fattest carcase. European Union regulations allow for 3 subdivisions in each fat class (Hickey et al., 2007). However in Ireland there are no divisions of fat class such as those described by Jones et al. (2004), and therefore the percentage subcutaneous fat categories were defined as follows; class 1 is b6%, class 2 is 6–9.99%, 3 is 10–13.99%, 4 is 14–18.99% and 5 is ≥19% subcutaneous fat. The value of genetic improvements in reduction in fatness was assumed to come from heavier carcase weight (minus the cost of feed to grow animals to heavier carcase weights) at the same end point fatness, rather than from premiums for lean animals. The reason for this being that the pricing system for carcase fat class in Ireland means that farmers tend to grow lambs to the same end point fatness, so they are slaughtered at a heavier carcase weight, rather than sell animals leaner. However, a proportion of carcases on any given farm are not likely to grow to the same end point fatness, and therefore not capture the value of additional carcase weight. There are two reasons for this: Firstly, late-born lambs are not likely to be carried to the extra carcase weight as feed supply declines and the cost of finishing increases (Keady, 2009). It was assumed that 34% of lambs, those slaughtered from the beginning of October onwards would be highly unlikely to be carried to heavier carcase weights and gain value from reductions in fatness. Secondly a proportion of lambs with a very low subcutaneous fat percentage are unlikely to be grown to the point where penalties would be incurred for over-fatness. Therefore it was assumed that animals grading 10% subcutaneous fat percent or lower will not capture value in reductions in fatness, and this proportion equates to 14% of animals. 2.6. Economic value of number of lambs born The method used to calculate the economic value for number of lambs born was based on a model developed by Conington et al. (2004) for UK hill sheep, which predicted

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lamb birth types (single, twin, and triplet) by equating the mean and variance of litter size to their expectations. At the current Irish industry average lambing rate of 1.5 lambs born per ewe lambing (Anonymous, 2007), predicted proportions of ewes bearing singles, twins, and triplets were 0.52, 0.46, and 0.02, respectively. The net economic return from each additional lamb born is dependent on the costs and revenues of lambs sold per ewe lambing (Conington et al., 2004), which are highly variable between lambs born as single, twins and triplets, and on different farm types. This variability is due to differences in lamb survival rates, lamb feed costs, proportion of lambs achieving seasonal premiums, ewe survival rates, ewe feed costs, and ewe fixed costs. A summary of these factors is presented in Table 3.

each case; difficulty in single-bearing ewes is likely to be due primarily to disparity between lamb size and the size of the birth canal, whereas in multiple-bearing ewes, difficulty is likely to be due primarily to complications associated with lamb presentation and multiple lambs in the birth canal. The total costs of each lambing difficulty from types (2) to (4) respectively outlined above, relative to no assistance are presented in Table 4. Economic values for single- and multiple-bearing ewe lambing difficulty were weighted by the proportion of ewes in the flock having singles and multiples, and in addition the economic value for multiple-bearing ewes was divided by the average number of lambs born as multiples, such that the economic value was presented per lamb born.

2.7. Economic value of lambing difficulty

2.8. Economic value of lamb survival

The economic value of lambing difficulty assumed an underlying normally distributed liability, partitioned by thresholds (Meijering, 1980) into categories defined as (1) no assistance, (2) voluntary assistance, (3) moderate assistance, and (4) significant assistance. Thresholds between categories were derived from data presented by Hanrahan (2008), with a genetic change in lambing difficulty modelled by assuming that the underlying normal distribution moved in relation to the thresholds. Lambing difficulty economic values were calculated by determining the effect of a 1% increase in the proportion of ewes requiring moderate assistance or worse on the proportion of ewes in each of the lambing difficulty categories. The change in proportions by category was then multiplied by the costs of each category relative to no assistance and aggregated to generate the overall economic value. Lambing difficulties for single-bearing and multiplebearing ewes were considered as separate traits due to differences in the primary underlying cause of difficulty in

The economic value of lamb survival expressed as the value per lamb surviving to slaughter was calculated assuming that 52% of ewes in the commercial flock had singles, 46% had twins, and 2% had triplets. Net returns per lamb surviving to slaughter were accounted for within each birth rank by applying the estimates for the proportion of carcases obtaining a seasonal premium, lamb feed costs based on number of days to slaughter, fixed costs, ewe feed costs (Table 3), and the cost of ewe deaths (Table 4).

Table 3 Lamb survival rates, lamb feed costs, seasonal premiums proportions, ewe survival rates, ewe feed costs, and ewe fixed costs for single, twin, and triplet lambs respectively.

Lamb survival Lamb feed costs (€) a, b Seasonal premium proportion c Ewe survival Ewe feed costs (€) d Ewe fixed costs (€) e

Single

Twin

Triplet

0.93 13.30 0.60 0.99 0 8.00

0.88 20.56 0.20 0.96 5 12.00

0.80 23.36 0 0.93 11 15.00

a Lamb feed costs are calculated assuming single, twin, and triplet lambs are weaned at 36, 32, and 31 kg and take on average 140, 170, and 180 days to reach slaughter, respectively. b This includes 50% of triplets (those assumed to be raised as triplets) being fed 30 kg of concentrate at a cost of €0.0192 per MJME. c The proportion of lambs from each birth type that are slaughtered early enough to attain seasonal premiums (defined as the average price per kg of carcase weight in the period of the year where earlier slaughter captures a premium — see Fig. 1). d Ewe feed costs are calculated based on the assumption that twin and triplet-bearing ewes are fed 18 and 42 kg of extra concentrate respectively, at a cost of €0.0192 per MJME. e Ewe fixed costs are incorporated at €8, €12, and €15 for single, twin, and triplet-bearing ewes respectively. These are estimates based on fixed costs for labour, housing, machinery, and miscellaneous (Keady, 2009).

2.9. Economic value of ewe mature weight An increase in ewe mature weight is expected to result in higher annual maintenance feed requirements for the ewe, higher feed requirements for maintaining and growing the replacement, as well as a heavier carcase weight of the cull ewe. The cost of annual maintenance feed per kilogram of additional ewe live weight was predicted from daily energy requirements for ewe maintenance presented in Nicol and Brookes (2007), and energy cost presented in Section 2.3. Metabolisable energy costs for animals grazing summer pasture were calculated at the opportunity cost of pasture fed to finishing lambs. In winter, ewes were assumed to be fed Table 4 The total costs of each lambing difficulty type from (2) to (4) respectively, relative to no assistance.

Stockman hours Stockman cost per hour (€) Veterinary costs (€) Proportion of time vet required Probability of a dead lamb Cost of a dead lamb (€) Probability of a dead ewe Cost of a dead ewe (€) Ewe disposal costs (€) Reduced reproductive success Barren ewe cost (€) Proportion of barren ewes getting second chance Lambing cost relative to no assistance (€)

Voluntary assistance

Moderate assistance

Significant assistance

0.20 17.00 0 0 0 0 0 194.51 20.00 0 154.90 0

0.85 17.00 0 0 0.15 0 0.01 194.51 20.00 0 154.90 0

1.75 17.00 90.00 0.30 0.25 0 0.20 194.51 20.00 0.25 154.90 0.23

3.40

15.52

129.66

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entirely on silage at a cost of €0.0175 per MJME. This included the direct cost of baled and wrapped silage (Section 2.3) and an assumption that 33% of this silage could have been fed to finishing lambs. Replacement females were fed an equivalent diet (feed type) to ewes (Section 2.3). It was assumed that the replacement ewe reaches mature weight after a second winter (24 months-of-age). The impact of a 1 kg increase in mature weight on replacement ewe feed requirements, and therefore the cost of replacement, was calculated as the increase in energy requirements for growth and maintenance (Nicol and Brookes, 2007) from birth to 24 months-of-age. Additional value captured from a heavier cull ewe carcase is calculated at a cull ewe price of €1.60 per kg of carcase weight with a dressing percent of 40%. Of all ewes (older than 21 months-of-age) slaughtered, 10% are downgraded to very low quality, where no premium is offered for extra carcase weight.

2.10. Economic value of foot rot In order to model the economic value of genetic improvement in resistance to foot rot and foot health-related problems, two farms with differing levels of foot rot prevalence (‘low’ and ‘high’) were considered. The costs associated with the required preventative measures and treatment protocols on these two farms provided the basis for differences in costs associated with changes in prevalence. Preventative measures and treatment protocols included foot bathing, topical and antibiotic treatments, and foot paring. Economic values were calculated for two separate traits, ewe foot rot and lamb foot rot, and presented as per percent of ewes or lambs with any foot score N0; it is assumed that the ‘low’ prevalence farm has 5% of ewes and lambs with any foot score N0, while the ‘high’ prevalence farm has 25% of ewes and lambs with any foot score N0. Foot rot was treated as a binomial trait as described by Conington et al. (2008). Farm data on foot rot prevention and treatment practices from Nolan et al. (2002) provided the variation in control strategies undertaken by these two farms and were assumed to occur as a direct result of the requirement to manage the condition, and therefore directly linked to prevalence. Table 5 presents the prevention and treatment measures implemented by the ‘low’ and ‘high’ prevalence farms. Labour costs (at €17 per

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hour) were included as the time taken per ewe or lamb for foot paring, foot bathing, topical and antibiotic treatments. Average footbath chemical costs were €0.07 per footbath per treated ewe, and €0.056 per footbath per treated lamb (Nolan et al., 2002). Labour costs equated to €0.09 per footbath per ewe, and to €0.06 per footbath per lamb. Chemical and labour costs per topical treatment equated to €0.35 (McHugh, 2009) and €0.28, respectively. Drug and labour costs per antibiotic treatment equated to €0.90 (McHugh, 2009) and €0.28, respectively. Labour costs for foot paring were calculated at €0.60 per ewe. 2.11. Discounted genetic expressions The reporting of sub-indices requires estimation of discounted genetic expressions for traits expressed at different times and frequencies. The method used for calculation of discounted genetic expressions was presented by Amer (1999), where individual estimated breeding values were used for individual traits and indices were expressed as the contribution of the sire to the profitability of progeny born. The discounted genetic expression model in this study assumed an average litter size of 1.5 lambs per ewe lambing, pre- and post-weaning lamb survival rates of 0.89 and 1.0 respectively, 0.41 of ewe lambs born per sire were kept as replacements, and ewe survival rates of 1.0, 0.93, 0.79, 0.52, 0.23, and 0.07 for 2, 3, 4, 5, 6, and 7 year old ewes respectively. 2.12. Genetic standard deviation Genetic parameters estimated for the Irish sheep population were inserted for carcase conformation class, carcase fat class (unpublished). Genetic standard deviation and heritability for days to slaughter were calculated from a growth rate trait from Irish industry data (unpublished). Genetic standard deviation for ewe mature weight was calculated assuming a mean of 72 kg, CV of 10.5%, and a heritability of 0.3. Lambing difficulty trait genetic standard deviation was derived assuming maternal variance proportions (m2) of 0.22. Genetic parameters for the remainder of the traits were estimated from known phenotypic variances and heritability estimates from the literature (Conington et al., 2001; Safari et al., 2005; Nieuwhof et al., 2008b). 2.13. Sensitivity analysis

Table 5 Foot rot prevention and treatment measures implemented by the ‘low’ and ‘high’ prevalence farms.

Annual foot parings (per ewe) Annual foot bathing (per ewe) Foot parings (per lamb) Foot bathing (per lamb) Proportion requiring spray treatment (ewes) (%) Proportion requiring spray treatment (lambs) (%) Proportion requiring antibiotic treatment (ewes) (%) Proportion requiring antibiotic treatment (lambs)

Low prevalence

High prevalence

0.33 1.67 0 1 5

5.67 9.00 0 3 10

5

10

3

6

0

0

Seasonal feed costs can vary significantly based on feeding regimes and the farming system in question (Conington et al., 2004), particularly those related to the opportunity cost of feed. Sensitivity analysis was carried out to determine the implications on the breeding objective of substituting pasture with maize crops and, alternatively, of basing the opportunity cost of pasture on its use for finishing beef cattle rather than for finishing lambs. Maize was priced at €0.20 per kg DM (+33%), assuming the crop could be purchased at €200 per tonne DM. Finishing beef cattle accounted for the potential opportunity cost of feed given to finishing cattle for slaughter at 20 months. In this scenario the opportunity cost of pasture was €0.10 per kg DM (−33%), assuming 100 MJME increased steer carcase weight by 0.45 kg (Nicol and Brookes, 2007),

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and the value of carcase weight was €2.54 per kg (BordBia, 2008). Economic weights were also tested for sensitivity to changes in the average number of lambs born per ewe lambing from 1.5 to 1.2 and 1.8, for changes ±25% in prime lamb carcase and ewe carcase price respectively, and for changes ±25% in hourly labour cost. 3. Results Table 6 describes the traits in 4 sub-indices including economic values, discounted genetic expression coefficients, and economic weights (economic values multiplied by discounted genetic expressions) multiplied by the genetic standard deviation, for each trait, in the units of expressions of the sires' genes per lamb born. An increase in days to slaughter by one day per lamb slaughtered had an economic weight (multiplied by the genetic standard deviation) of −€1.41. The model for carcase conformation predicted the average value of a one point change, on the 15 point scale, to be €1.02 per carcase. Converting this value to a per class parameter produced an economic weight of €0.35 per carcase. An increase in subcutaneous fat of 1% was associated with a 1.34 kg increase in carcase weight and therefore is equivalent to 15.3 days of growth, assuming a carcase growth rate of 88 g per day. The predicted changes in carcase weight per percent change in fat score calculated here are similar to those presented in Lewis et al. (1996). Seasonal premium loss and feed costs over 15.3 days, accounting for proportions of slaughter lambs not capturing value due to reductions in fatness, and adjusting to fat class, produced an economic value of −€3.26 per 1% increase in fat per carcase. This translated to an economic weight per fat class of −€0.52. Single, twin, and triplet lamb net carcase sale values were calculated at €47.16, €36.02, and €31.53, respectively. The economic weight for number of lambs born was calculated as

€0.83 for each extra lamb born at the industry average of 1.5 per ewe lambing. Separate economic weights calculated for lambing difficulty for single- and multiple-bearing ewes were −€0.69 and −€0.37 respectively, for a 1% increase in ewes requiring moderate assistance or worse. The economic weight of lamb survival was €1.15 for each lamb surviving to weaning. An increase by 1 kg in ewe mature weight was predicted to increase ewe maintenance energy requirements by 49 MJME per annum and increase maintenance and growth energy requirements for a replacement by 151 MJME. The economic values of mature weight for ewe maintenance feed requirements and replacement feed requirements were −€0.70 and −€2.00, respectively. The economic value of cull ewe value as influenced by ewe mature weight was €0.46 per kilogram of live weight at slaughter. Combining these into an economic weight for ewe mature weight (to account for differences in timing of expression of the component traits) produced a figure of −€1.49/kg of additional mature weight. Economic weights of ewe foot rot and lamb foot rot were calculated at −€0.82 and −€0.09 per ewe or lamb per year per 1% with a foot score N0 at any single assessment at a time of year when foot rot is prevalent. Differences in the economic weights for lamb and ewe foot rot can be attributed to a comparatively short time on farm, reduced treatment levels, and therefore reduced cost of lamb foot rot compared to ewe foot rot. The results of sensitivity analysis based on the opportunity cost of pasture, prime carcase price, ewe carcase price, and hourly labour cost are presented in Table 7. A change in the opportunity cost of pasture resulted in a proportional change in the economic weight for days to slaughter and ewe mature weight. Because the value of genetic improvements in reduction in fatness was assumed to come from heavier carcase weight (at the same end point fatness), the economic weight for fat class

Table 6 Economic values, discounted genetic expressions, and calculation of economic weights (economic values multiplied by discounted genetic expressions) multiplied by the genetic standard deviation, for breeding objective traits in sub-indices, in the units: expressions of the sires' genes per lamb born. Goal trait group (sub-index)

Objective trait

Production

Days to slaughter a Carcase conformation b Carcase fat c Lambing difficulty direct Single-lambing ewes Multiple-lambing ewes Lamb survival (direct) e Number of lambs born f Ewe mature weight g Lamb foot rot h Ewe foot rot i

Lambing

Maternal Health a b c d e f g h i

DGE

σG

EW*σG

€ 3.05 −€ 3.26

0.46 0.46

15.16 0.25 0.35

−€ 1.41 € 0.35 −€ 0.52

−€ 0.247 −€ 0.132 € 39.70 € 19.53

0.57 0.57 0.57 0.27

−€ 0.0129 −€ 0.2207

0.51 0.27

4.92 4.92 0.05 0.16 4.11 13.83 13.83

−€ −€ € € −€ −€ −€

Economic value

d

Per day. Per EUROP carcase conformation class. Per EUROP carcase fat class. Per 1% increase in the percentage of ewes requiring moderate assistance or worse. Per lamb surviving to slaughter. Per lamb born. Per kilogram of mature weight. Per 1% increase in lambs with a foot score greater than zero. Per 1% increase in ewes with a foot score greater than zero.

0.69 0.37 1.15 0.83 1.49 0.09 0.82

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Table 7 Sensitivities of the economic weights to price changes in the opportunity cost of pasture, prime carcase price, ewe carcase price, and hourly labour cost. Objective trait a

Base

Days to slaughter Seasonal premium Feed cost savings Carcase conformation Carcase fat Lambing difficulty direct Single-lambing ewes Multiple-lambing ewes Lamb survival (direct) Number of lambs born Ewe mature weight Lamb foot rot Ewe foot rot

−€ 1.41

a

Opportunity cost of pasture

Prime carcase price

Ewe carcase price

Hourly labour cost

+ 33%

+ 25%

+ 25%

+ 25%

−€ 1.76

−€ 1.66

−€ 1.41

−€ 1.41

€ 0.35 −€ 0.52

€ 0.35 −€ 0.28

€ 0.35 −€ 0.73

€ 0.35 −€ 0.52

€ 0.35 −€ 0.52

−€ −€ € € −€ −€ −€

−€ 0.69 −€ 0.37 € 0.96 € 0.45 −€ 1.69 −€ 0.09 −€ 0.82

−€ −€ € € −€ −€ −€

−€ 0.69 −€ 0.37 € 1.15 € 0.83 −€ 1.46 −€ 0.09 −€ 0.82

€ 0.76 € 0.40 € 1.15 € 0.83 −€ 1.49 −€ 0.10 −€ 1.00

0.69 0.37 1.15 0.83 1.49 0.09 0.82

0.69 0.37 1.52 1.16 1.68 0.09 0.82

See Table 6 for trait units.

was sensitive to changes in the opportunity cost of pasture, increasing (less negative) proportionally to the change in opportunity cost of pasture. The net return of each additional kilogram of carcase weight diminished when the cost of energy was increased; there was therefore less value in reducing fatness, at a higher opportunity cost of pasture. A significant decrease by almost 50% (from €0.83 to €0.45 per lamb born) was observed in the economic weight of number of lambs born when the opportunity cost of feed was increased by 33% (maize feeding). With maize feeding instead of pasture, the net values of additional lambs born were €42.32, €28.41, and €24.16, for single twin and triplet lambs, respectively. The economic weight of a 1% improvement in lamb survival expressed per lamb born showed a linear relationship with changes in the opportunity cost of pasture, driven by changes in the net value of lambs of each birth rank. An increase by 25% in the prime carcase price influenced traits days to slaughter, carcase fat class, number of lambs born, lamb survival, and ewe mature weight. Changes in prime carcase price showed a linear relationship with the economic weights for all these traits. An increase by 25% in the ewe carcase price had a linear relationship with the economic weight for ewe mature weight. An increase by 25% the hourly cost of labour had a linear relationship with the economic weights for lambing difficulty (single- and multiple-bearing) and lamb and ewe foot rot. The economic weight of additional lambs born was highly dependent on assumptions regarding the additional costs and potential loss of revenue associated with extra lambs born. Reducing the industry average number of lambs born per ewe to 1.2 (from 1.5) had no effect on the economic weight for the lambs born trait, or the lamb survival trait. The reason for this is that the prediction equations have to assume that at average lambs born per ewe lambing below 1.5 (and above 1), there is a direct exchange of a single lamb for a twin a lamb and so the economic weight is constant. By increasing the industry average to 1.8 the economic weight of number of lambs born was reduced from €0.83 to € €0.74, while the economic weight of additional lambs surviving reduced only slightly from €1.15 to €1.08.

4. Discussion 4.1. General This paper determines economic weights, using a variety of methodologies, for a wide range of traits, and provides a basis from which a robust comparison of selection candidates can be made. The trait-by-trait approach taken in the calculation of economic weights presents a departure from the complete system models integrated across traits used by Conington et al. (2004), Kosgey et al. (2003), and Wolfová et al. (2009b). Importantly, this breeding objective will also provide a directional focus for the development of breeding schemes and provide commercial farmers with tools to assist in making ram purchasing decisions. Traits with high positive economic weights were lamb survival and to a lesser extent number of lambs born, while traits with highly negative economic weights were days to slaughter (i.e. higher growth rate is positive) and ewe mature weight (i.e. heavier ewe weight is negative). This is indicative of the significant value of improving the ability of lambs to survive to weaning, without increasing lambing percentage. The highly negative economic weights for both days to slaughter and mature size indicate a powerful unfavourable relationship between these traits. This unfavourable correlation indicates a possible opportunity to benefit from the incorporation of breeding objectives targeted towards specialised sire and dam lines, particularly with such a negative economic value for mature weight. The economic weights of number of lambs born and lamb survival are sensitive to changes (increases) in the industry average number of lambs born. However, more prolific flocks have an increase in the number of DGE for lamb birth traits. This can have implications for the economic weights of both the number of lambs born trait and the lamb survival trait, and in particular the ratio between them. The ratio of economic values (not accounting for DGE) of number of lambs born to lamb survival at an industry average lambs born of 1.5 was 1:2, whereas at 1.8 lambs born it was 1:2.14. This represents a reduction in the value of number of lambs born relative to lamb survival with increasing number of lambs born, although the economic values for both traits

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were lower, as shown above. When the equivalent ratio was calculated with the economic weights (accounting for the increase in DGE) the difference in the ratio was significantly higher (20%). Therefore, the increase in DGE, as a result of higher numbers of lambs born, increases the economic weight of lamb survival relative to number of lambs born.

4.2. Comparison with other studies A number of studies have investigated the economic impact of changes in performance for traits linked with profitability in meat sheep production systems. For example Amer (1999) and Jones et al. (2004) examined specific maternal and terminal aspects of production, respectively. Other studies used whole farm system bio-economic models including dairy sheep (Wolfová et al., 2009a,b) and non-dairy sheep (Wolfová et al., 2009c), UK Hill sheep (Conington et al., 2004), and sheep in the tropics (Kosgey et al., 2003, 2004). Under the assumption that lambs will be slaughtered at the same carcase weight the value of faster growth is in a reduction in the number of days taken for the lamb to reach target slaughter weight. Studies in cattle (Amer et al., 1997, 2001) and sheep (Kosgey et al., 2003; Conington et al., 2004; Jones et al., 2004) have focused attention on carcase weight as a trait to measure increases in growth rate. With an understanding that slaughter plants in Ireland do not want heavier carcases, days to slaughter offers a measure of increased growth rate that captures growth performance pre- and post-weaning and a goal trait that commercial farmers understand. Using a normal distribution threshold model, economic weights favouring reduced fatness and improved conformation in UK hill sheep reported by Conington et al. (2004) were minimal when compared to those calculated here. Interestingly, those results showed that the premium for an increase in a unit conformation class is roughly equivalent to the penalty for increasing fatness by a whole fat class, whereas in this study the premium for an increase in conformation by one class is roughly equivalent to two thirds of the penalty for fat class. This difference can be explained by the assumption that the value of leanness is captured by selling lambs leaner, for which the carcase premiums are relatively low compared with carcase weight (Conington et al., 2004). In contrast Wolfová et al. (2009b) derived negative economic weights for conformation class, and commented that the skewed distribution of slaughter animals over grade classes meant that conformation traits would be of negligible value in a selection program for dairy sheep. Amer et al. (2001) noted that economic weights for carcase traits derived using grading thresholds and price differentials result in economic weights which are highly dependent on the mean carcase characteristics of a group of commercial animals at the time they are slaughtered. Variation can be expected in the economic weights for carcase traits between breeds, sexes, and finishing systems for commercial calves (Amer et al., 1998). It is likely that this variation will also be seen in lambs; however information on the variation in average conformation class and fat class for a range of individual commercial farms was not available. It should be noted that improved conformation tends to be associated with increased fatness

(Lewis et al., 1996) except when it is associated with a superior muscling genotype (Campbell and McLaren, 2007). Sheep production in Ireland can be broadly grouped into lowland and hill farming systems. Physical limitations which present constraints to improvements in production dictate the likely value of genetic improvement for particular traits in these two production systems. These physical limitations are particularly evident in hill sheep production systems which represent a harsher environment. In the Irish sheep industry the number of lambs born can be considered a trait with an economic optimum. The economic weight of number of lambs born calculated in this study shows diminishing returns with an increase in flock average number of lambs born, reflecting the significant increase in costs associated with an increase in triplets as average litter size increases. This is in agreement with the results presented by Amer (1999) for New Zealand sheep flocks, Conington et al. (2004) for UK hill sheep, and Wolfová et al. (2009c) for non-dairy sheep. The economic weight of lamb survival is highly sensitive to current average lambing rate, which affects the proportions of ewes having singles, twins, and triplets in the flock. More prolific flocks have a higher proportion of lower value lambs and the economic value per lamb surviving is lower. While in this paper we assume constant rates of lamb survival within each birth and rearing litter size combination, flocks managed in harsher environments with lower effective levels of lamb survival will achieve lower economic returns from genetic improvement of number of lambs born at all levels of prolificacy (Amer et al., 1999; Kosgey et al., 2003; Conington et al., 2004). The ratio of economic weights of number of lambs born to lamb survival calculated here (0.73:1) is very different from that calculated by Wolfová et al. (2009c) (1.37:1). This difference represents a significant contribution to the index of lamb survival relative to increasing the number of lambs born in the Irish sheep industry. Conington et al. (2004) stated that to improve ewe productivity, selection emphasis might be better moved away from improving litter size and towards more improved lamb survival. No studies have predicted economic weights for lambing difficulty. Nevertheless, similar models have been used to predict the value of genetic change in calving difficulty in Ireland (Amer et al., 2001) and in the UK (Amer et al., 1998). The ratio of economic weights of lambing difficulty to lamb survival is calculated at 0.6:1 and 0.32:1 for single- and multiple-bearing lambing difficulty respectively. These are slightly less than the ratio of number of lambs born to lamb survival (0.73:1), and further indicate the importance of lamb survival, relative to other traits in the index. The economic weight calculated for ewe mature weight in this study was strongly negative. The ratio of economic weights of mature weight to lamb survival is calculated at 1.3:1. Wolfová et al. (2009c) calculated economic weights for ewe mature weight which was only very slightly negative, when compared to that calculated here; the ratio of economic weights of mature weight to lamb survival being 0.14:1. However in the Wolfová et al. (2009c) study no account was taken of the costs associated with replacement growth to a heavier mature weight. Indeed Conington et al. (2004) excluded additional feed cost in growing replacements, and

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subsequently estimated a smaller negative economic weight for this trait. Average mature ewe weights incorporated in both these studies were much lower than that in the current study, contributing to a reduction in the economic weight of mature weight in these breeding objectives. Interestingly, Kosgey et al. (2004) derived a positive economic weight for ewe mature weight under tropical conditions but ewes were assumed to mature at 30 kg. Intangible benefits such as financing, insurance and risk aversion were included in the model, and most importantly, feed costs were assumed negligible and ignored. A number of studies have investigated eradication and control strategies (Ennen et al., 2009; Winter, 2009), validation of scoring methods (Conington et al., 2008), and the impact foot rot has on production (Nieuwhof et al., 2008a). This paper presents the development of economic weights for foot rot. The ratio of economic weights of ewe foot rot to lamb survival is 0.71:1, showing that foot rot is an important trait affecting profitability in the Irish sheep industry. 4.3. Implications In the early stages of the development of the genetic improvement program for sheep in Ireland, the breeding objectives in this paper provide a directed emphasis for trait recording, selection strategies, and mating systems, in addition to the determination of how valuable genetic improvements in a specific trait would be. Breeding objectives presented in the form of four goal trait groups have been developed to summarise breeding value information. The aim of the goal trait groups is to simplify selection decisions for commercial terminal and maternal sheep producers. These indices provide commercial farmers with the ability to adjust breeding emphasis towards specific market outcomes or address key production aspects of their particular farming system. In the future an index for hill sheep breeders may be justified. Acknowledgements Funding support for this study was provided by Sheep Ireland and the Irish Cattle Breeding Federation Society Ltd from the National Development Plan, administered through the Irish Department of Agriculture, Fisheries and Food. The authors would also like to thank members of the Irish sheep industry and Irish research organisations for their valuable input in this study. References Amer, P.R., 1999. Economic accounting of numbers of expressions and delays in sheep genetic improvement. New Zealand Journal of Agricultural Research 42, 325–336. Amer, P.R., 2006. Approaches to formulating breeding objectives. 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil. Amer, P.R., Emmans, G.C., Simm, G., 1997. Economic values for carcase traits in UK commercial beef cattle. Livestock Production Science 51, 267–281. Amer, P.R., Crump, R., Simm, G., 1998. A terminal sire selection index for UK beef cattle. Animal Science 67, 445–454. Amer, P.R., McEwan, J.C., Dodds, K.G., Davis, G.H., 1999. Economic values for ewe prolificacy and lamb survival in New Zealand sheep. Livestock Production Science 58, 75–90.

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