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* NEIKER, Basque Institute for Agricultural Research and Development, PO Box 46, 01080 Vitoria-Gasteiz, Spain
Animal Breeding and Genetics Group, Wageningen University, PO Box 338, 6700 AH Wageningen, the Netherlands
1 Corresponding author: elmaturana{at}neiker.net
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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18.03/cow per calving interval per liability unit. This value was relatively insensitive to changes in the market price of animals but was more sensitive to changes in the incidence of dystocia. Records from parities between 1995 and 2002 were used for the estimation of genetic parameters for yield (actual milk, fat, and protein yield) and CE using a multivariate model. Linear sire models for yield traits and a threshold sire-maternal grandsire model for CE were used. A Holstein population was simulated to determine the consequences of including CE in the breeding goal. Three selection strategies were considered: 1) selection only on yield traits, 2) selection on yield and direct CE (DCE), and 3) selection on yield, DCE, and maternal CE (MCE). Selection on yield traits only resulted in a slight reduction of dystocia. Selection strategies in which DCE or DCE and MCE were included in the breeding goal did not improve the genetic response for DCE and MCE obtained with the first selection strategy. Genetic responses were also calculated using the 2.5th, 50th, and 97.5th percentiles of posterior densities of genetic correlations between DCE and MCE and yield traits. Because responses in CE were sensitive to deviations in estimates of genetic parameters, the inclusion of CE in the monitoring scheme is recommended. Genetic evaluation of bulls for CE is of considerable value because it provides farmers with the opportunity to use assortative matings of sires with favorable estimated breeding values for DCE to primiparous cows.
Key Words: dystocia • economic value • breeding goal • selection
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Deriving economic values is an important step in the optimization of breeding schemes because the breeding goal is a function of genetic merit of individual traits and their economic values. Several authors have derived economic values for CE (Bekman and van Arendonk, 1993; Dekkers, 1994; Albera et al., 1999). However, the economic values for CE reported in these studies have differed, depending on costs and production conditions.
Since 1992, CE records have been collected systematically in the Holstein dairy cattle population of the Basque Country Autonomous Region, Spain. A routine genetic evaluation of sires for CE has been in operation since 1995 (Alday and Ugarte, 1997). More recently, several studies have been conducted to optimize the genetic evaluation system (our unpublished results, 2003). At present, a sire-maternal grandsire threshold model (Van Tassell et al., 2003) is used in routine genetic evaluations of CE. Although farmers consider dystocia as one of the most important problems in their production system, the economic importance of dystocia in the population has not yet been studied. The main objectives of this study were 1) to estimate the economic value of CE and 2) to determine the impact of different breeding goals on the genetic response for CE and yield traits in the Basque Holstein dairy cattle population.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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where 
KGM, KGF, KGP, DCE, and MCE correspond to the economic weights for KGM, KGF, KGP, DCE, and MCE, respectively, and GKGM, GKGF, GKGP, GDCE and GMCE correspond to the breeding values for KGM, KGF, KGP, DCE, and MCE, respectively.
The following assumptions were made for the breeding goal:
Economic Model
Herd Management.
Economic values were determined for a herd involved in milk yield and heifer rearing. Female calves were kept in the herd as replacement heifers, and male calves were sold 1 mo after birth. Animals were assumed to be slaughtered after they were culled from the farm. The decision to cull, in this case, was made in the last stages of the lactation, leaving the cow in the herd until the optimal time for replacement during that lactation (van Arendonk, 1988). The replacement policy followed by most farmers was to enter a pregnant heifer into the milking herd.
Information Source.
Field data for yield and CE were provided by EFRIFE (Federation of Holstein Associations from the Basque Country Autonomous Region of Spain). Economic data were provided by the Service Cooperative for each Holstein association.
Data on CE were collected monthly by trained technicians at the same time as milk recording. Each calving was scored as follows: 1 = unassisted; 2 = slight assistance; 3 = needed assistance; 4 = cesarean caused by calf size; 5 = cesareans for other reasons, such as malformations or posterior or abnormal presentations of the calf. Calving ease information was matched with data collected in the AI recording system, which includes dates of all services, and also with data from the milk recording system.
Records from parities between 1995 and 2002 and data from the first 8 lactations were used in the analysis. Only cows with a complete set of yield, insemination, and CE records were included in the data set.
Contemporary groups for yield traits, formed as the interaction between herd and calving year, had to comprise at least 5 records (Ugarte et al., 1992). Days in milk levels within parity effect were defined because of large differences in lactation lengths (308 ± 96). For each parity (first parities, second parities, and third and greater parities), 11 levels of DIM were defined, ranging from 0 to 550 d. Lactations longer than 550 d were not considered, because they could be a result of unregistered abortions. Age levels within parity effect were also defined. For first-parity cows, the age groups were <26, 26 to 29, and >29 mo of age; for second parities, the age groups were 31 to 36, 37 to 40, and >40 mo of age; and for third or higher parities, <54, 54 to 62, and >62 mo defined the respective age groups.
The following editing procedures were carried out: Calvings scored as 5 were not considered because these lack a CE genetic component; gestation lengths longer than 294 d or shorter than 264 d were not considered, to eliminate incorrect calving dates; heifer calving ages at less than 18 mo or more than 40 mo were discarded, as were multiparous cow calving ages less than 28 mo or more than 206 mo. Contemporary groups for CE, formed as the interaction between herd, year, and the technician who scored the record, had to comprise at least 5 records (Ugarte et al., 1992). Classes 3 and 4 were joined into one group because of the low percentage of data scored as 4 (Moreno et al., 1997). After editing, the data set available and used in the analysis consisted of 32,171 records, from 17,326 cows distributed across 560 herds. The percentage of animals in each CE category is shown in Table 1
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Values for INS were taken from a previous study (López de Maturana et al., 2006) in which the effect of each category of CE on the number of inseminations needed for pregnancy in the next reproductive cycle was estimated.
Losses in milk, fat, and protein yield attributable to CE were estimated using the GLM procedure of SAS (SAS Institute, 1996). The effects of CE on each cost factor are summarized in Table 1
. Economic values used in the calculation of actual costs for each CE category are given in Table 2.
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where C is the average cost of CE; 
(t) is the cumulative standard normal distribution; ti – µ is the distance between mean liability and threshold ti in units of the standard normal liability scale; ci is the costs of CE for each score (2 = slight assistance, 3 = difficult calving); [(t2 – µ) – (t1 – µ)] is the incidence of the second category; and [1 – (t2 – µ)] is the incidence of the third category.
The economic value of CE can be computed by partial differentiation of the cost function with respect to the population mean for the liability scale (Meijering, 1986):
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Levels of dystocia and market prices of animals were either increased or decreased by 50%, and economic values were estimated again under the new situation to appraise their sensitivity to production circumstances. Because milk production traits are already included in the breeding goal and to avoid double counting, a reduced economic value excluding costs associated with losses of milk yield was estimated for MCE.
Economic Values of Yield Traits.
The economic values for milk, fat, and protein yield (i.e., 0.12, 0.92, and 3.64/kg per calving interval per cow) were taken from González-Recio et al. (2004), who estimated the values for the same population while taking into account quota conditions.
Estimation of Genetic Parameters
Model.
Linear sire models were used for yield traits and a threshold sire-maternal grandsire model was used for CE. Animal models including maternal effects were not adopted because reaching convergence of the Gibbs sampler for categorical traits was difficult, as reported previously by Luo et al. (2001). To be consistent, a sire-maternal grandsire model was used for CE, whereas a sire model was used for yield traits.
In matrix notation, the model is:
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where yKGM, yKGF, yKGP, and yCE are vectors of observations for KGM, KGF, KGP, and CE, respectively, and bKGM, bKGF, bKGP, and bCE are vectors of systematic fixed effects. For KGM, KGF, and KGP, systematic effects were the contemporary group (herd-calving year, with 2,466 levels for both traits), DIM classified in different levels within parity (33 levels), the month of calving (12 levels), and the parity-age effect (9 levels). For CE, bCE contained the effects included in the routine genetic evaluation: contemporary group (herd-calving year-technician, with 3,244 levels), the month of calving (12 levels), parity-sex of calf (4 levels), and the breed of service sire (2, Holstein and non-Holstein). peKGM, peKGF, peKGP (with 17,326 levels) are vectors of random permanent environmental cow effects for yield traits. No permanent environmental effects were considered for CE. Therefore, the permanent environmental effects follow a multivariate normal distribution of mean 0 and variance H0, where H0 is
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asKGM, asKGF, asKGP, and asCE are the vectors of random sire genetic effects for yield traits and CE, respectively; amgsCE is a vector of maternal grandsire genetic effects for CE; and asKGM, asKGF, asKGP, asCE, and amgsCE are considered to follow a multivariate normal distribution with mean 0 and variance G0 
A, where
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and A is the numerator relationship matrix among sires. eKGM, eKGF, eKGP, and eCE are the vectors of residual terms for the four traits. They are jointly distributed following a multivariate normal distribution of mean 0 and variance R0, where
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XKGM, XKGF, XKGP, XCE, WKGM, WKGF, WKGP, ZsKGM, ZsKGF, ZsKGP, ZsCE, and ZmgsCE are the known incidence matrices with the appropriate dimensions for each trait.
Implementation.
A genetic parameter estimation was carried out by Bayesian inference using Gibbs sampling with the data augmentation technique (Albert and Chib, 1993), as described by Sorensen and Gianola (2002). The prior distributions for the fixed effects and (co)variance parameters were uniform, bounded between 106 and –106. The full posterior conditional distributions for the location parameters (fixed and random effects) were univariate normal. The full posterior distributions for variance components were inverted Wishart distributions.
In each Gibbs sampling analysis, a unique chain of 300,000 iterations was used, discarding the first 50,000 samples and retaining every 50th sample. Thus, 5,000 samples were used to compute posterior means and standard deviations. The length of burn-in period was assessed by visual examination of trace plots of the estimates of genetic parameters. Features of the marginal posterior distributions were obtained using the Bayesian output analysis package (available in http://www.public-health.uiowa.edu/boa). Because of the use of sire and sire-maternal grandsire models, direct genetic variances for each trait and maternal genetic variance for CE were calculated following the procedures described by Kriese et al. (1991) and Wiggans et al. (2003).
Breeding Scheme
Selection in a Holstein dairy cattle population was simulated in SelAction (Rutten et al., 2002) to determine the consequences of including CE in the breeding goal in a multitrait pseudo-BLUP selection index. We used a conventional progeny testing program for improvement of milk yield in dairy cattle with the use of AI and assumed that cows reproduce naturally (i.e., without embryo transfer). In this scheme, young bulls are tested by mating to a random sample of cows, the resulting heifers are reared, and their first lactation and calving performance are recorded. The daughter lactation information is then used to produce a genetic evaluation on each young bull (called "first proof"). At this stage, the best bulls can be selected for breeding and the remainders discarded. In contrast, heifers and cows are evaluated mostly based on their own lactation performance.
Population Structure.
An open nucleus population with overlapping generations was modeled following the methodology developed by Bijma et al. (2001). Phenotypes of selection candidates were recorded prior to reproductive age and EBV were calculated. For males, progeny information was included in their EBV. These progeny were assumed to be born outside the nucleus, so their dams were not considered in the breeding value estimation. For females, only their own performance information on yield traits and MCE were used in the breeding value estimation.
In overlapping generations, animals were assigned to different age classes and may have been selected to produce offspring more than once. Culling because of age was considered to be random so as not to affect genetic (co)variances. Therefore, the number selected from each age class was determined by truncation selection on EBV across age classes. Each sire was mated at random to dams, and each dam produced a fixed number of offspring (0.5 of each sex). The time difference between 2 consecutive classes was 1 yr. The selection candidates were 160 young bulls produced annually and a total population of 30,000 cows. Ten years was the maximum age for both sexes, because the replacement rate was 20% for the cow population per year.
To produce 160 young bulls, the number of selected parents was calculated as follows:
The progeny of young bulls was set to 100 females.
Discounting Expressions.
The number of expressions of CE and production traits was assumed to be equal. This reflects a situation in which each lactation starts with the birth of a calf and the difference in timing between cows giving birth and producing milk is small. However, the time difference between the cow being born itself and producing milk is larger, and this is ignored. Accounting for differences in cumulative discounted expression between traits in the breeding goal is expected to increase the relative economic merit of calving difficulties slightly compared with that of production, but this is expected to have a negligible effect on the results.
Genetic Responses.
Three different scenarios were considered (I to III):
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6 yr, the progeny test information on yield traits was also used. 3 yr, their own performance information on yield traits was also used.
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6 yr, progeny test information on yield traits and DCE. 3 yr, their own performance information on yield traits.
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6 yr, progeny test information on KGM, KGF, KGP, DCE, and MCE. 3 yr, their own performance information on KGM, KGF, KGP, and MCE. Selection responses were predicted using SelAction software (Rutten et al., 2002). Responses for DCE and MCE for scenario I and responses for MCE for scenario II were estimated as correlated responses.
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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. The cost associated with the second CE class was 31.94/ cow per calving interval. The cost associated with the third CE class was 155.19/cow per calving interval. The average cost of CE, taking into account the proportion of each category and the total costs by class, was 26.22/cow per calving interval (Table 4). Milk yield losses and labor costs accounted for nearly 60% of the average costs associated with calving problems.
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18.02/ cow per calving interval per unit. This value is negative because higher proportions of dystocia produce an increase in costs. The economic value of DCE was, in absolute and relative terms, different from those estimated by other authors (e.g., Bekman and van Arendonk, 1993; Albera et al., 2004), which is not surprising given the differences in production systems, market prices, and cost items considered. Similar to findings of Meijering (1986), the economic value of DCE in the Basque Holstein population was more sensitive to changes in the incidence of dystocia than to changes in market prices (Table 4). A proportional increase in the incidence of dystocia by 50% resulted in a 14% increase in the economic value. However, a 50% increase in market prices of animals increased the economic value of DCE by only about 6%. This is probably because only a small proportion of the total costs of CE relate to replacement costs of animals (see Tables 2 and 3).
The resulting economic value for MCE was –13.25/ cow per calving interval per unit. This economic value was lower than the one for DCE because the costs of losses in milk were not taken into account in its calculation to avoid double counting.
Estimation of Genetic Parameters
Estimated genetic parameters are shown in Tables 5 and 6. Heritability estimates for KGF and KGP were both 0.27 and were similar to estimates obtained in the same population by Pérez-Cabal and Alenda (2003). Heritability estimates for KGM (0.25) and estimates of genetic correlations between yield traits (see Table 6) were lower than those found by Pérez-Cabal and Alenda (2003). In that study, yields were standardized to 305 d, whereas in the present study, the actual yields per lactation were used. Posterior means of heritability estimates for DCE and MCE were low (0.09 and 0.10), and within the range of values reported in other studies (Djemali et al., 1987; Wiggans et al., 2003). The estimated genetic correlation between direct and maternal calving ease was –0.46. A negative association was also reported by other authors (Djemali et al., 1987; Wiggans et al., 2003) and reflects the genetic antagonism between direct and maternal effects.
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Genetic Response
Table 7 shows the general trends obtained for each breeding goal when considering the mean of the posterior density distributions for rgYCE after 10 yr of selection. The genetic responses for DCE and MCE expressed in the liability scale were transformed to the observed scale, following Dempster and Lerner (1950). Both DCE and MCE improved slightly (–0.19%) when selection was only for yield traits, which was in agreement with the results obtained by Meijering (1984). After 10 yr of selection, the expected incidence of DCE and MCE was 2.32%. The inclusion of DCE or DCE and MCE in the breeding goal had a minor effect (<0.01%) on the total selection response, which agrees with the results of Dekkers (1994). This could be explained by the low genetic (co)variances estimated for CE compared with those estimated for yield traits. It can be concluded that there is little extra gain to be expected from including CE in the breeding program. The low genetic response obtained for CE may be affected by the low reliabilities of EBV for MCE and DCE. However, genetic evaluation of bulls for CE is still of considerable value, because it provides farmers with the opportunity to use assortative matings of sires with favorable EBV for DCE to primiparous cows.
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). Genetic responses of selection for each breeding goal using the posterior medians for rgYCE were the same as those obtained considering the posterior means for rgYCE. Genetic responses for DCE and MCE differed greatly when the extreme values of the posterior densities for rgYCE were used. For estimates corresponding to the 2.5th percentile, the expected incidence of both DCE and MCE was 1.21% after 10 yr, and was similar for all 3 breeding goals. For estimates of the 97.5th percentile, and using the first and second breeding goals, the expected incidences for DCE and MCE were 4.21 and 3.91%, respectively, after 10 yr of selection. For the third breeding goal, the expected incidence for both DCE and MCE would be 4.21%. The contributions of DCE and MCE to the total response, considering the third breeding goal and the estimates of the extremes of the posterior densities for rgYCE, were higher than those found considering the posterior means or medians for rgYCE. However, the contributions of DCE and MCE were still low (around 1%). Given these results, it is recommended that CE be included in the monitoring scheme to obtain more accurate estimates of genetic parameters.
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6.2 and –4.6. |
CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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ACKNOWLEDGEMENTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Received for publication July 3, 2006. Accepted for publication January 15, 2007.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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This article has been cited by other articles:
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  O. Gonzalez-Recio, E. Lopez de Maturana, and J. P. Gutierrez Inbreeding Depression on Female Fertility and Calving Ease in Spanish Dairy Cattle J Dairy Sci, December 1, 2007; 90(12): 5744 - 5752. [Abstract] [Full Text] [PDF]
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g2) and phenotypic (