Economic values for reproduction traits in beef suckler herds based on a calving distribution model

LIl/ETOCK P-N Livestock Production

Science 46 (1996) 85-96

Economic values for reproduction traits in beef suckler herds based on a calving distribution model P.R. Amer Scottish Agricultural

*, B.G. Lowman,

G. Simm

College, West Mains Road, Edinburgh EH9 3JG, UK Accepted 28 March 1996

Abstract Economic values for the genetic improvement of gestation length, post partum anoestrus interval, conception rates at first or later post partum oestrus and calving day (relative to the first day of the calving season) in seasonal calving beef suckler herds were estimated. The approach used combines partial budgeting of the economic cost of a barren cow with a model of the herd calving distribution which is driven by assumed levels of reproductive parameters. Economic values for al1 traits were shown to be highly variable across farms and are also likely to vary across years. For an increase in gestation length and equivalently in post partum anoestrus interval, estimates of economic values ranged between -0.2 and -2.77 f/day with higher values apparent in herds with a wide calving spread. Economic values for conception rates were higher at later (0.25 to 1.28 E per % increase) than at first (0.1 to 0.7 f per % increase) post partum oestruses. For calving day, economic values were approximately half of those derived using an alternative method from the literature which assumes calving day to be normally distributed. Implications for establishing selection criteria to choose terminal beef sires and suckler beef cows are discussed. Keywords:

Economic

value; Beef; Reproduction

1. Introduction Although performance recording of pedigree beef cattle has been in operation for over 20 years in the UK, multi-trait selection indices were introduced only relatively recently (Allen and Steane, 1985). The indices were intended for use primarily in terminal sire breeds. The selection objective was to maximise the financial margin between saleable meat yield produced and food consumed, taking into account the cost of calving difficulty. More recent adoption of sophisticated statistical tools (best linear unbiased prediction) for genetic evaluation of beef cattle (Grump et al., 1994) has opened the way for a more detailed definition of the economic merit of terminal sires. In particular, with the dramatic shift in sire breed usage towards large continental types, there is an important need to understand how calving problems affect the subsequent reproductive performance of the suckler herd, and the economic consequences of an alteration in reproductive performance.

* Corresponding

author.

0301-6226/96/$15.00 Copyright PII SO301-6226(96)00016-4

0 1996 Elsevier Science B.V. All rights reserved.

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P.R. Amer et af. / Livestock Production Science 46 11996) 85-96

Herd reproductive performance might also be improved by direct genetic selection on an appropriately defined trait in suckler cows and their sires. Lopez de Torre and Brinks (19901, Meyer et al. (1990), and Rege and Famula (19931, among others, have reported low to moderate he&abilities for potential selection criteria such as calving date, first breeding day, calving rate and calving interval. Heritability estimates for calving date tend to decline after first calving (Meacham and Notter, 1987; Buddenberg et al., 1990; Rege and Famula, 1993) suggesting that this trait may in part reflect genetic variation in the onset of puberty. Alternatively, environmental variation in calving date may be greater for later calvings. Relationships between calving difficulty in heifers and reproductive traits such as calving interval and conception rates have been reviewed by Morris (1980) and are typically highly significant. Service sire has been shown to be an important source of variation in the calving day of cows in seasonal calving beef herds (Azzam and Nielsen, 1987; Buddenberg et al., 1990). Considerable use of AI in the UK beef industry means that data are available for direct estimation of breeding values for gestation length, and other potential selection criteria relating to suckler cow reproduction. Optimal use of this information for selection purposes requires that the economic consequences of changes in clearly defined reproductive traits must first be quantified (Hazel, 1943). The objective of this study was to define the economic values for improvements in gestation length, days to first oestrus, conception rate and calving day in seasonal calving suckler herds. A second objective was to explore potential variability in economic values across farms and years.

2. Methods Gestation length, days to first oestrus and conception rates influence herd calving patterns. In herds with a specified mating period and with seasonal calving, alteration of these variables influences the proportion of barren cows. Three sections below describe the methodology used in this study. In the first section, a model of the annual calving distribution of a suckler herd is developed. The model predicts calving distributions and the proportion of the herd which will be barren. Partial budget calculations of the cost per barren cow are described in the second section while definition and computation of economic values is described in the third section. 2.1. Calving distribution model The reproductive parameters influencing the calving distribution are highly variable across animals and farms. The model was therefore constructed in such a way so as to allow for variability among animals on a farm using probability theory. Alternative farm circumstances were evaluated through manipulation of the model driving variables. The distribution of previous conception da_tes P,(d,) was approximated using a beta probability density function scaled to have a mean calendar day d, and a range of L, the specified length of the breeding season. Shape parameters for the beta distribution (01= 1.2 and p = 1.8) were chosen because they resulted in the most realistic approximation to the expected pattern of calving days. It was also assumed that both gestation length (GL) and the post partum anoestrus interval (PPI) follow normal distributions with means p,cr, l.~rri and variances u& , a& respectively. The phenotypic covariance between GL and PPI was assumed to be zero. However, because the model depends on the variance of the sum of GL and PPI, this assumption can easily be relaxed. Sensitivity of the model to the assumed variance of the sum of GL and PPI is considered in the results. Combining previous conception day, GL and PPI distributions gave the probability density of the calendar day of first oestrus (F) as follows:

P,(d,)=jm

P,(d,-dc).N(dc,CLGL+rUppIl &+~;&~dc~ --m where iV(y, p,, a’) is a normal density function for a variate y with mean p and variance a’, and d,, the day of first oestrous is expressed as the number of days from the herd mean for previous conception day.

P.R. Amer et al. /Livestock Production Science 46 (1996) 85-96

87

Let d’ be the first day of mating expressed relative to the herd mean for previous conception day. The distribution PF(dF) can now be partitioned according to the number (i = 0 to n) of oestrus cycles of length OC exhibited prior to d’ as follows:

i=lton,d’-(i-l)OC>dr>d’-((i)OC elsewhere

i=n,

d/d’-(n)OC

where F, is the probability of a cow exhibiting first oestrus on day dF, given that she exhibits i oestruses in total prior to dJ. The probability distribution of next conception days (PO), which is conditional on d’, was then computed as = k e F,[(d,, ;=a

Pcl(dc,ldJ)

j=

-OC(i+j-

l))ldJ]

.NR,,j

1

where m is the maximum possible number of matings (j) per cow and NR,, is the zjth element of a matrix NR of proportions of cows conceiving after the jth (columns) mating for cows exhibiting oestrus i-l (rows) times before the mating period. Taking an example where conception rates to inseminations at first (CR,) and later (CR) post partum oestruses are 0.4 and 0.7, respectively, and a maximum possible 5 matings per cow, NR would be as follows:

For a given set of specified model parameters, dJ must be assigned a value, dJ *, so that an annual calving cycle is maintained for a length of the breeding season L. An annual calving cycle occurs when the mean of the distribution of next calving days is 365, where those theoretical conception days which occur after the end of the mating period, and which therefore correspond to barren cows, are not included in the mean. For any value of d’, the mean day of conception for non-barren cows can be approximated as:

In practice, DC tends to be sufficiently linear in d’ to allow computation of d’* using linear interpolation between several estimates of the mean calving day at different values for d”. In principle, further iterations of the model can be calculated by substituting P,,(d,-,ldJ* > for PJd,) in the computation of PF(dF) and repeating the calculations. However, because P,,(d,, Id”* ) can not be derived in a closed form, this results in successive nesting of integration steps so that the degree of computation necessary rapidly becomes intractable. The proportion, Pa, of barren cows was approximated as d*+L

P, = 1 -

c d=d’

P,,(d,,Id’*).

P.R. Amer et al. /Livestock Production Science 46 (1996) 85-96

88

2.2. Barren cow cost

For simplicity, it was assumed that the suckler herd manager would chose to maintain a barren cow and forego profits from rearing the calf, rather than to replace barren cows with an in-calf heifer. This was in line with typical management practices for the UK, with the exception of cows which are older than 10 years of age, or are barren for two successive years. The proportion of barren cows in any single year which fit either of these exceptions is expected to be low, especially with cows to be culled due to their age normally not being exposed to the bull. Table 1 summarises the calculations of calf revenue forgone, net of calf concentrate and roughage costs saved, for four common suckler herd categories in the UK. Detailed financial records collected over 204 herds in 1992 (MLC, 1993) form the basis of the calculations. In addition, roughage costs adjusted for seasonal differences, were estimated based on age and live weight at sale for the various categories. After accounting for roughage costs, the estimated cost per barren cow was in the region of E 230 across a11four herd categories (Table 1). 2.3. Economic values Let PB( R) be the proportion of the herd barren, given the level of a reproductive variable R and 6~/6P,

be

Table 1 Estimated cost of a barren suckler cow for four different farm types Herd location and calving season lowland spring Herd details ’ Number of herds Calf revenue (E/cow mated) Calf concentrate (f/cow mated) Cows barren (No./100 mated) Calving period (days for 90%) Calf age at sale (days) Calf weight at sale (kg) Estimated Estimated Estimated Assumed Assumed Estimated

calf forage costs total calf feed intake (kg DM) b calf forage intake (kg DM) ’ days summer feeding days winter feeding calf forage costs (E/calf) d

Net cost (f/cow

barren) e

107 291 4.7 5.3 86 223 285

upland autumn

32 353 13.3 5.3 93 309 353

spring

41 287 2.7 5 91 216 256

autumn

24 389 26 6.3 93 396 376

1920 1855 223 0 74

2464 2278 160 150 157

1688 1650 216 0 66

2648 2284 216 180 153

227

253

233

229

* Source, MLC (1993). b Calculated as 8 (calf sale weight - 45) assuming a standard feed conversion ratio of 8 kg of DM intake per kg live weight gain and a birth weight of 45 kg. ’ Calculated as estimated calf feed intake - 14(calf concentrate cost) taking into account the price and percentage dry matter of concentrate. d Calculated assuming forage costs of 4 and 10 p/kg DM in summer and winter respectively and weighting by the proportion of days summer and winter feeding. e Calculated as [ lOO(calf revenue - calf concentrate)/cows barren] - calf forage costs.

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P.R. Amer et al. /Livestock Production Science 46 (1996) 85-96

the cost to the suckler herd per barren cow (& 230). The economic value (EV) per cow of a unit change in a reproductive variable (R) can be calculated as

EV(R)=

SP,( R) 6R

&T .6p, B

when P,(R) is linear or when 6P,( R)/6R corresponds to an infinitely small change in R from a known and constant population phenotypic mean for R. Because large changes in reproductive variables might also be of interest, because mean values for the reproductive variables vary over years and from farm to farm, and because the proportion of barren cows is unlikely to be linear in any variable R, a more general approach to the calculation of economic values was used and is described below. Table 2 contains four sets of base parameters corresponding to herds which differ in CR, CR,, PPI and L. All parameters in Table 2 tit within ranges across diverse production systems reported in the scientific literature. For each herd, 9 model estimates of percent barren cows corresponding to 9 values for each reproductive variable (CR,, CR and GL) were obtained. The 9 values were equally spaced between 0.24 and 0.56 for CR,, 0.4 and 0.8 for CR and between 280 and 296 days for GL. Linear regressions of the percent of the herd barren on the top, middle and bottom 5 values in each range were then multiplied by f 230, the cost per barren cow, to obtain economic values. Differences in economic values estimated using the three subsets of results are intended to reflect likely variation in the herd phenotypic means across years for each variable. Economic values for PPI are equivalent to those computed for GL and so only results for GL are presented. With reductions in either trait, on average cows are ready to breed earlier in the breeding season, and therefore get more opportunities to conceive. Economic values for calving day (CD), defined as the day of calving relative to the first day of the breeding season, were also computed for each of the four herds using the model. Because variation in CD reflects variation in the reproductive variables already considered, it is not appropriate to include calving day in any set of breeding objectives containing CR CR or GL. However, it is useful to compare economic values for CD with estimates from other studies. Calving day distributions Pc,(d,,) were derived for each herd by continuing the model as follows: r

,

By retrospectively assuming that all cows calving after a specified day dT will in fact have been barren (Ponzoni and Newman, 19891, the economic value can be calculated as EV(adT)

= -$/=p,,(dT)8dT. d

Table 2 Parameters

used to simulate the conception

$

=Pc,(dT)

distributions

B

and proportion

Parameter

Abbreviation

Mating period (days) Post partum interval mean (days) Post partum interval sd. (days)

L

Conception rate (first post partum oestrus) Conception rate (subsequent cycles) Oestrus cycle length (days) Gestation length (days) Gestation length sd.

-;

B

IJ‘PPI UPPI

CR, CR oc (LGL OGL

of cows barren for four different herds Herd A 70 40 5.75

0.4 0.55 21 288 5

Herd B 98 40 5.75 0.4 0.55 21 288 5

Herd C 98 60 5.15 0.4 0.70 21 288 5

Herd D 126 60 5.75 0.4 0.70 21 288 5

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P.R. Amer et al. /Livestock Production Science 46 (1996) 85-96

such that Pcr,(dT> is implicitly assumed to be the partial genetic regression (James, 1981) of the barren cow rate on calving day. It is important to choose dT such that the implied proportion of barren cows is appropriate to the situation under which the economic value is to apply. This proportion for any value of dT has to be approximated from the model as dT+ 100 &3(dT)

=

c

&VT).

d*

Economic values for CD assuming normality of CD and which correspond to the method of Ponzoni and Newman (1989) were also calculated. Calving day standard deviations were assumed to be 15, 20, 20 and 25 days for herds A, B, C and D, respectively. Assuming a linear genetic regression of unity for days open on calving day, the economic value for calving day might be used as a proxy economic value for days open. The economic value for PPI is likely to underestimate the economic value of days open because the latter implicitly accounts for variation in conception rates in addition to variation in PPI.

3. Results Figs. l-3 show the effects of CR,, CR and GL on the percent of barren cows for the four herds. Over most of the parameter ranges shown, the highest barren cow rates were observed in herds A and C indicating the important relationship between PPI and L. With a short mating period and a high PPI, there is less opportunity to get cows calving late back in calf. As the mating period gets longer, there are fewer cows calving so late in the breeding season that they do not have time to get back in calf.

2

01

.24 .28 .32 .36 .40 .44 .48 52 ,516 Conception rate at first pp. oestrous

Fig. 1. Percent barren cows in four herds in relation to conception herd C - diamonds, herd D - squares.)

rate to the first postpartum

oestrus. (Herd A - triangles, herd B -

circles,

91

P.R. Amer et al. / Liuestock Production Science 46 ( 1996) 85-96 18 16

4

2 0

.4 .45

.5

55

$6 65

.7

.75

.8

Conception rate Fig. 2. Percent barren cows in four herds in relation to conception - circles, herd C - diamonds, herd D - squares.)

rate to other than the first postpartum

oestrus. (Herd A - triangles, herd B

16

14

12

,

2 80282284286288290292294296

Gestation length Fig. 3. Percent barren cows in four herds in relation to herd mean gestation diamonds, herd D - squares.)

length. (Herd A - triangles,

herd B - circles,

herd C -

92

P.R. Amer et al./Livestock

Table 3 Economic values (f/l% conception

change) for conception

Herd

A Et C D

Table 4 Economic values (f/l% low rates of conception

rate (CR,)

Production Science 46 (1996) M-96

at first post partum oestrus in four herds at either average, high or low rates of

Average

High

Low

CR,

CR,

CRF

0.12 0.11 0.49 0.34

0.13 0.10 0.34 0.21

0.13 0.13 0.70 0.49

change) for conception

rate (CR) at second and later post partum oestrus in four herds at either average,

Herd

Average CR

High CR

Low CR

A B C D

0.96 0.50 0.82 0.60

0.60 0.25 0.68 0.45

1.28 0.75 0.86 0.70

high or

Differences between herds in the effect of CR,, CR and GL (or PPI) on barren cow rates as shown in Figs. l-3 are important because they affect the economic values. Barren cow rates for herd A appeared to be the most affected by changes in CR and the least affected by changes in CR,. The relatively compact calving period and short PPI mean that herd A probably has the highest proportion of cows cycling before the beginning of mating and so matings to the first post partum oestrus are probably least for this herd. Barren cow rates in herds C and Table 5 Economic values (f/day) PPI

for gestation length (GL) or for interval to first post partum oestrus (PPI) in four herds with average, high and low

Herd

Average GL/PPI

High GL/PPI

Low GL/PPI

A B C D

-0.31 - 0.29 - 1.81 - 1.17

-0.37 - 0.37 - 2.71 - 2.22

-0.2 -0.2 -0.92 - 0.56

Table 6 Economic values (f/day) for calving normal calving day distribution

day (CD) in four herds assuming

5% or 10% barren rates, derived from the model or assuming Normality

assumption

a

Herd

Model 5% barren

10% barren

5% barren

10% barren

A B C D

0.50 0.44 0.63 0.60

0.84 0.85 1.13 1.02

1.58 1.19 1.19 0.95

2.70 2.02 2.02 1.62

a The standard deviation

for calving day was assumed to be 15 days for herd A, 20 days for herds B and C, and 25 days for herd D.

a

93

P.R. Amer et al./ Livestock Production Science 46 (1996) 85-96

A

,’

/’

/----

_-- -___ --._ %.

%.

:

,:\ ! f 1

% ‘,,..

“..,,,

,,,

‘,. . . . . . . . ..(.

I

I

I

30

60

90

“‘.,. ..,,,,,, ,““..

..,..

I

120

“,:“I.

150

Calving day Fig. 4. Calving day distributions for herd A (solid) and herd C (broken). Dotted lines correspond conceived after the end of the mating period when assuming no variation in gestation length.

to cows which would have to have

D are most affected by GL (or PPI). These herds have lower proportions of cows showing oestrus before the beginning of the mating period, and for which there is no advantage of a shorter gestation length. Table 3 shows economic values for CR, for each herd, expressed per 1% change (e.g. the value of changing CR, from 40 to 41%) per cow, per breeding season. Economic values for CR per 1% change are in Table 4. Differences in economic values at high, average and low herd mean conception rates directly reflect the non linearity shown in Figs. 1 and 2. The results show that the cost of a genetic reduction in either CR, or CR will be higher in difficult breeding years while it also appears that across herds, average economic values might differ by 100%. For GL (or PPI), variation in the economic values was even more apparent (Table 5). The range in economic values across herds was 5-fold. Economic values for CD assuming either 5% or 10% barren cow rates and calculated using the model are in Table 6. Economic values derived assuming calving dates to be normally distributed (Ponzoni and Newman, 1989) are also in Table 6. Fig. 4 shows calving day distributions for herds A and C derived from the model. Where the distributions are shown with dotted lines, most cows would have to have conceived after the end of the normal mating periods for these herds. Compared to the negatively skewed calving distributions derived from our model (Fig. 41, the assumption of normality overestimates the rate at which the proportion of barren cows is reduced with an earlier herd average calving day (Table 6). The degree of overestimation ranged between approximately 60 and 200% for the examples considered. Table 7 shows the effects of the standard deviation assumed for the variance of the sum of GL and PPI on the model predictions of the proportions of barren cows for the four different herd types. These effects are small, relative to the differences in barren cow rates across the four herd types.

Table 7 Sensitivity interval

5.4 7.6 11

of model predictions

of the percent barren cows to assumed variances for the sum of gestation length and post parturn anoestrous

Herd A

Herd B

Herd C

Herd D

8.1 8.4 8.6

3.5 3.6 3.8

10.4 10.6 11.0

6.6 6.8 7.3

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94

4. Discussion 4.1. Economic values This study has shown that the economic values for improvements in cow fertility traits depend on the phenotypic values of the traits. This was due to the nonlinear relationships between the reproductive variables and barren cow rates derived from the model. Definition of breeding objectives is therefore complicated because the phenotypic values for reproductive traits are expected to vary across farms and years and might also change with genetic progress. Because variation across years can not be predicted, it is best to use averages over several years as input parameters for the model. Most research relating to non linear breeding objectives has focused on determining economic values which optimise the economic returns from breeding over a specified planning horizon. However, the gains relative to readjusting breeding objectives on a generation by generation basis are usually trivial (Pastemak and Weller, 1993; Groen et al., 1994; Dekkers et al., 1995). From this study, a more important question relates to the problem of differing economic effects of trait changes across farms. Groen (1989) assessed the effects of variable production circumstances on revenues from dairy cattle breeding programs and the potential for diversification for the national breeding goal. However, diversification was only justified in anticipation of a dramatic shift in the milk quota and pricing system. For beef cattle, the practical question remaining is how to assist specific herd managers in identifying bulls at market and replacement cows which are best for their herd with respect to expected reproductive performance while simultaneously allowing for variation in performance of other economically important characters. Further research is needed to address this question. Interactions may exist between genetic levels of reproductive traits and optimal management. In this instance, results from the model could be incorporated into a mathematical programming routine to model changes in optimal farm management with genetic changes as suggested by Amer and Fox (1995). With reoptimisation of management, increases in economic values might be expected, although these effects should be quite small when compared to the effects of expected differences in economic values across farms discussed above. 4.2. Comparisons

with other approaches

Other studies on the economic effects of changes in beef herd reproductive parameters have focused on barren cow rates (Newman et al., 1992; MacNeil et al., 1994) or cow weaning rate (Bar-wick et al., 1994) rather than investigating the effects of changes in parameters which ultimately determine barren cow rates. It could be argued that economic values for barren cow rates can be derived using simple assumptions as in Table 1, and they avoid the complexity of nonlinear relationships identified in this study. There is also very limited scope for direct measurement of, and selection on, individual service conception rates or the length of the postpartum anoestrus interval in practical breeding programs. However, because the complexities are ignored, rather than circumvented, with these approaches there is a danger that the underlying reproductive traits will not be weighted optimally when making selection decisions. Gestation lengths can be measured directly in pedigree breeding herds when use of artificial insemination is frequent. However, the economic effects of a change in GL are usually considered in relation to genetic relationships with calving ease and calf mortality alone (e.g., Jansen et al., 1984), and the relation to the effective length of the breeding season for a sizeable proportion of the herd, as examined here, is ignored. The size of some economic values for gestation length shown in Table 4 suggests that, for some herds, the effect of shorter gestation length sires on the effective length of the breeding season may be worthy of consideration in addition to any advantages from reduced calving difficulty. Ponzoni and Newman (1989) describe a selection index for beef cattle in Australia which includes the day of calving relative to the first day of the calving season (CD), rather than barren cow rate, as a trait in both the breeding objective and in the selection criteria. In the context of selection index theory, comparisons between

P.R. Amer et al. /Livestock Production Science 46 (1996) M-96

95

the two depend on the amount of genetic variation in each, the extent to which this variation can be exploited through selection on the traits themselves or on correlated traits, and their economic values (Ponzoni, 1992). Variation in calving day represents additional variation in potential cow fertility in the non-barren proportion of cows. However, some form of ad hoc estimation is required to obtain an estimate of hypothetical calving day for barren cows (Ponzoni and Newman, 1989). The non-normal shapes of the calving day distributions derived in this study emphasises concern about the assumptions of normality in the estimation of economic values for CD (Ponzoni and Newman, 1989). The negatively skewed and ‘lumpy’ calving day distribution created by the model (Fig. 4), and also observed in UK field records, results in economic values for CD about half the size of those under the assumption of normality (Table 6). This would result in calving day having considerably less importance in selection than implied by the results of Ponzoni and Newman (1989). 4.3. The model The model developed in this study forces a much more rigorous understanding of how reproductive variables affect calving distributions and barren cow rates for seasonal calving suckler herds than has been used in other studies of economic values for reproductive traits. However, because of the intensive computing procedures involved, a number of approximations were incorporated into the model. The assumption that the previous distribution of conception dates follows a beta distribution is clearly a simplification. One alternative is to continue the model through an additional iteration using the conception date distribution from the first iteration as the starting distribution for the second iteration. However, this had only trivial effects on the estimates of barren cow rates and consequent economic values while at the same time computational requirements were dramatically increased due to the successive nesting of formulae involving iteration. No consideration was taken of the possibility of having heifers calving earlier than the main herd. If economically feasible, this might tend to alleviate reproductive problems and lower absolute economic values, particularly for GL or PPI, although no account was taken of the likely longer PPI and lower conception rates for heifers after their first calf which would increase economic values in absolute terms. Expected values for reproductive variables were assumed to be independent of each other, and of time. While the size of, or effects on the model, of phenotypic correlations among CR, CR,, GL and PPI for animals within a herd are almost certainly trivial (e.g., Table 71, a number of studies in New Zealand and the United States have reported significant negative regressions of PPI on calving date (Morris et al., 1978; Bellows and Short, 1978; Montgomery et al., 1980; Pleasants and McCall, 1993). When known, these effects can be incorporated into the model with minimal additional effort. However, variation is likely to be considerable across farms and years.

5. Conclusions

The economic values estimated in this paper open the way for a much more rigorous definition of breeding objectives with respect to reproductive variables of beef suckler cows than has been previously possible. This is important, first, in the selection of terminal sires which can affect suckler herd reproduction through their effect on gestation length. This affects both the effective length of the breeding season and fertility through its association with calving difficulty. Secondly, tbe economic values are important when determining the advantages of direct selection on reproductive variables to improve the economic merit of suckler herds. The results highlight the need for theoretical studies into how variation in economic values across farms can be accounted for, and also point to some deficiencies in previous approaches to estimate the economic value of earlier calving dates.

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P.R. Amer et al. /Livestock Production Science 46 (1996) 85-96

Acknowledgements Support for P.R.A. from the GB Meat and Livestock Commission is gratefully acknowledged. We are also grateful to two anonymous referees for useful comments on draft manuscripts. SAC receives financial support from the Scottish Office Agriculture, Environment and Fisheries Department.

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