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* Dept. of Dairy and Animal Science, The Pennsylvania State University, 324 Henning Building, University Park, PA 16802
Dairy Record Management Systems, 313 Chapanoke Rd. Suite 100, Raleigh, NC 27603
Corresponding author:
Chad Dechow; e-mail:
cdechow{at}tennessee.edu.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Key Words: body condition score loss • heritability • production • reproduction
Abbreviation key: BCSL = body condition score loss, DFS = days to first service, ME = Mature Equivalent, SPC = services per conception
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Genetic parameters for BCS have been reported by several authors (Veerkamp, 1998; Jones et al., 1999; Dechow et al., 2001; Koenen et al., 2001). Cows genetically inclined to have higher BCS during the lactation are reported to have fewer days to first service (DFS), fewer services per conception (SPC) and a shorter calving interval than cows that are genetically thin (Pryce et al., 2000; Dechow et al., 2001; Pryce et al, 2001). The genetic correlation between energy balance and first luteal activity was reported to be moderately negative after adjustment for yield (Veerkamp et al., 2000). Additionally, bulls that sire daughters with high dairy form scores (and likely more angular and thin) have daughters with higher incidences of metabolic, reproductive and foot and leg diseases (Rogers et al., 1999; Hansen et al., 2002).
Direct estimates of the heritability of body condition score loss (BCSL) and the genetic relationship among BCSL, production and reproductive performance are limited. The heritability of BCS change from week 1 to week 10 of lactation was reported to be 0.09 in an experimental herd (Pryce et al., 2001). Additionally, genetic correlation estimates between BCS measured at various points during the lactation has been reported to be high, indicating that genetic variation for BCSL may be limited (Dechow et al., 2001; Jones et al., 1999; Koenen et al., 2001). Body condition score loss from week 1 to week 10 of lactation was reported to be genetically correlated with higher yield, and extended DFS, days to first heat and calving interval in an experimental herd (Pryce et al., 2001).
The genetic relationship between BCSL and BCS has not been defined, but may be important to understand to the impact of selection for yield on energy balance and BCS. The objectives of this study were to estimate the heritability of BCSL and estimate genetic and phenotypic correlations among BCSL, BCS, production and reproductive performance in commercial dairy herds.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Mature equivalents (ME) for milk, fat and protein production, DFS and two sources of SPC were available. The first source of SPC was used to investigate the genetic and phenotypic relationship between BCSL and SPC, whereas the second source of SPC was used to investigate the phenotypic relationship between SPC and DFS.
Services per conception for genetic analyses were recovered from cows that had conceived and subsequently calved. Records from second and higher lactations reported the number of times a cow had been inseminated in the previous lactation. Thus, SPC in first lactation was obtained from a cow’s second lactation record. Likewise, SPC in second lactation was recovered from a cow’s third lactation record. Cows that had conceived, but not subsequently calved, would not have a SPC record from this source.
Lactation records reported a cow’s pregnancy status, if known, and the number of times that cow had been inseminated. Approximately 10% of cows were confirmed pregnant and had both SPC and DFS recorded. These records were used to investigate the phenotypic relationship between DFS and SPC.
The initial data set included 310,071 lactation records. Not all lactation records had BCS data available. Records were edited to include those cows with a valid identification, a registered Holstein sire and a Holstein dam. Valid birth dates and calving dates were required and lactations initiated by an abortion were eliminated. First lactation cows that had calved prior to 20 months of age or later than 36 months of age were eliminated, whereas second lactation cows were required to have calved no earlier than 10 months and no later than 24 months after first calving. Records were required to have a ME milk of at least 4536 kg. Services per conception records were edited to include only those cows that required fewer than 10 inseminations to conceive, whereas DFS records were edited to include those cows that were first served between 25 and 200 days after calving.
In total, records for at least one trait were available for 51,195 cows after edits. The number of observations and mean of ME Milk, DFS and SPC are reported in Table 1
. Not all cows that had production or reproductive performance data available had BCS data available.
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. The number of cows with BCSL, mean BCSL, average age at calving, average calving interval, and the number of sires and herds represented for cows with BCSL are also reported in Table 2.
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Heritabilities, genetic and phenotypic correlation estimates among BCS at all six scoring periods, production and reproductive performance were previously reported by Dechow et al. (2001) using the same data. The focus of the current study was to investigate the relationship among BCSL in early lactation, BCS, production and reproductive performance.
It was determined that BCS at calving and postpartum BCS were the most suitable scoring periods to investigate BCSL in early lactation for two reasons. First, postpartum BCS had more observations available than BCS at first service. Secondly, the mean BCS at pregnancy check was higher than that of postpartum BCS or BCS at first service, indicating that early lactation BCSL had ceased and that external body fat was beginning to be deposited by pregnancy check. The average postpartum BCS for all cows that had postpartum BCS available was 2.91 in first lactation and 2.82 in second lactation (Table 2
). The average BCS at pregnancy check was 2.96 and 2.92 in first and second lactation, respectively. Of those cows that had BCS available at both postpartum and pregnancy check, the mean postpartum BCS was 2.89 in first lactation and 2.8 in second lactation, while BCS at pregnancy check averaged 2.96 and 2.93 in first and second lactation, respectively. Body condition scores were available at both calving and postpartum for 7424 cows in first lactation and 6092 cows in second lactation.
Analyses
The traits included in the genetic analyses included BCS at calving and postpartum, BCSL in early lactation, three production traits (ME milk, ME fat and ME protein) and two reproductive traits (DFS and SPC). Body condition score loss was defined as BCS at calving minus postpartum BCS. Higher values for BCSL represent more loss of BCS in early lactation.
Heritabilities, genetic and phenotypic correlations among BCSL and production, reproductive performance and BCS were estimated using a series of bivariate analyses. Analyses were performed with the average-information algorithm of the derivative-free REML program (Meyer, 1998). Standard errors for genetic correlations were calculated according to Falconer and Mackay (1996).
The basic statistical model used in the analyses was:
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where
| y | = | a vector of BCSL and one of the following: BCS at calving or postpartum, ME milk, ME fat, ME protein, DFS, or SPC,
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| age | = | age at calving in months,
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| b | = | vector of regression coefficients on age at calving in months,
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| hysi | = | vector of ith fixed effects for herd-year-season of calving,
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| animalj | = | jth random animal effect, and
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| eij | = | a vector of normally distributed random residuals.
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Sire identification for all cows and dam identification for most cows were available and included in the pedigree for any cow with a record for one or more traits. Maternal grand-sire identification was also available and included in the pedigree as sire of the dam. Sire and maternal grandsire pedigrees were traced for five generations and all ancestors were included in the pedigree. The final pedigree included all 51,195 cows from 5390 sires and 43,488 dams. A total of 100,718 animals were included in the pedigree when ancestors were included.
All models for second lactation traits also included the length of the prior calving interval as a covariable. Non-production traits (BCSL, BCS, DFS, SPC) were analyzed with and without ME milk as a covariable.
The season of calving effects were defined as January through April, May through August and September through December. Since the number of DIM when postpartum BCS was recorded was not known, DIM was not included in the model. However, DIM when postpartum BCS was recorded should be consistent within a herd and would be recorded before first service.
Because BCSL was derived from BCS at calving and postpartum, genetic correlation estimates between BCSL and BCS at calving or postpartum could be biased by part-whole influences when one trait is a function of another. Therefore, genetic correlations were estimated using two approaches. First, correlations between BCSL and BCS were estimated by allowing cows to contribute observations for both BCSL and BCS at either calving or postpartum, depending on which BCS trait was being analyzed. All cows that had an observation for BCSL, by the definition of BCSL, would also have observations for BCS at calving and postpartum.
Second, genetic correlations between BCSL and BCS at calving or postpartum were estimated only through pedigree linkages. Cows with BCS available at both calving and postpartum contributed BCSL observations, as in the first method. However, cows with BCSL observations were not allowed to contribute an observation for BCS at calving or postpartum. Only cows that had an observation available for BCS at calving and no postpartum BCS observation available (thus BCSL could not be calculated) contributed records for BCS at calving. Likewise, only cows that did not have a record for BCS at calving contributed records for postpartum BCS. Genetic correlation estimates between BCSL and BCS would then be through relationships in the pedigree described above between a group of cows with BCSL and a separate group of cows with BCS observations. There was no residual covariance between BCSL and BCS with this approach because no cows had observations for both traits.
The second approach allows for estimation of genetic parameters free of any part-whole influences that might otherwise impact parameter estimates when one trait is a function of another trait. Using both approaches should give a reasonable estimate of the genetic relationship between BCSL and BCS and not simply reflect the definition of BCSL used in this study.
The number of DIM when a cow is inseminated is likely to impact the success of that insemination. Adjusting SPC for DFS may result in more accurate correlations between SPC and other traits, particularly if those traits are correlated with DFS. Unfortunately, the number of cows with both SPC and DFS data in the same lactation was too small to facilitate accurate genetic analyses. Attempts to perform genetic analyses with the second set of SPC observations and DFS either did not converge or resulted in solutions at the boundary of the parameter space. Sufficient observations were available to determine the phenotypic relationship between SPC and DFS however.
Multiple regression was performed with ASREML (Gilmour, 2000) to determine the phenotypic relationship between DFS and SPC. The model used to investigate the relationship between SPC and DFS was:
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where
| y | = | SPC,
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| age | = | age at calving in months,
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| b1 | = | regression coefficient on age at calving in months, b2 to b5areregression coefficients on polynomials of order 1 to 4 for DFS,
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| hysi | = | ith fixed effect for herd-year-season of calving, and
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| ei | = | random residuals.
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A minimum of five cows per HYS group was required. Fourth-order polynomials of DFS were significant (P < .05) in first and second lactation, whereas fifth-order polynomials were not (P > 0.24).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Mean BCS are reported in Table 2. Mean BCS at calving was 3.18 in first lactation and 3.07 in second lactation. Mean postpartum BCS were 2.91 and 2.82 in first and second lactation, respectively. Of those cows that had both BCS at calving and postpartum BCS, an average of 0.30 and 0.29 BCS was lost in early first and second lactation, respectively. Heritability estimates for BCSL ranged from 0.05 to 0.07 in first lactation and from 0.01 to 0.03 in second lactation.
Genetic correlation estimates between BCS and BCSL are reported in Table 3. When cows were allowed to contribute both BCS at calving and BCSL observations, genetic correlation estimates ranged from –0.11 to –0.29. Genetic correlations were stronger (negative) when estimated through pedigree linkages only, ranging from –0.24 to –0.48.
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Phenotypic correlations between BCSL and BCS at calving ranged from 0.53 to 0.55. Phenotypic correlations between BCSL and postpartum BCS ranged from –0.62 to –0.69.
Correlations between BCSL and production traits are reported in Table 4. Increased BCSL was correlated with increased ME milk, fat and protein yield both genetically and phenotypically. Genetic correlation estimates ranged from 0.17 to 0.50, whereas phenotypic correlations ranged from 0.06 to 0.10.
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. Genetic correlation estimates between BCSL and DFS were positive in first and second lactation before and after adjustment for ME milk, ranging from 0.29 to 0.68. Phenotypic correlation estimates were not as strong, but were still positive, ranging from 0.05 to 0.09.
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A plot of the regression of SPC on DFS is shown in Figure 1. In general, SPC decline as DFS increase until around 175 DIM. The average SPC within a HYS group for cows that were first served at 175 days was approximately half the SPC required at 25 days.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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The heritability of BCS change from week one to week ten was reported to be 0.09, while the heritability of BCS was 0.28 at week one and 0.27 at week ten in a research herd (Pryce et al., 2001). Over a range of studies, genetic correlation estimates between BCS measured at different points during the lactation are strong. Genetic correlation estimates between BCS at the beginning of lactation and BCS at the end of lactation were reported to be 0.69 by Jones et al. (1999), 0.99 and 0.87 by Koenen et al. (2001), and 0.84 and 0.93 by Dechow et al. (2001). Genetic correlation estimates in all three studies tend to be highest for BCS measured in consecutive months or stages of lactation.
A second factor likely contributing to the low heritability for BCSL in this study was the inability to account for the DIM when postpartum BCS was assigned. This was assumed to be the major factor contributing to lower heritability estimates for BCS compared to other estimates in Dechow et al. (2001). Presumably, postpartum BCS would have been assigned after calving and before first service, but that may not be the practice in all herds, especially those that do not record BCS at first service. While the genetic component contributing to BCSL does not appear to be high, small genetic differences between cows may exist in the amount of BCS lost during early lactation. Jones et al. (1999) reported differences in the shape of the average daughter BCS curve for six sires with >1500 daughters using random regression models.
Phenotypically, an increase in BCS at calving was associated with more BCSL in early lactation in this and other studies (Treacher, 1986; Garnsworthy and Jones, 1987). Genetically, an increase in BCS at calving was correlated with less BCSL during early lactation. Management and environmental conditions that increased BCS at calving resulted in more BCSL in early lactation. However, cows that were genetically inclined to have higher BCS at calving appeared to maintain more BCS in early lactation than genetically thin cows.
The genetic and phenotypic relationship between postpartum BCS and BCSL was strong and negative. Management and environmental conditions that limited loss of BCS in early lactation resulted in higher postpartum BCS. Likewise, cows that were genetically inclined to have relatively high postpartum BCS tended to lose less BCS in early lactation.
There were differences in the magnitude of genetic correlation estimates between the two approaches used to estimate genetic correlations between BCS and BCSL. The definition of BCSL used in this study forced phenotypic correlations between BCSL and BCS at calving to be positive, while phenotypic correlations between BCSL and postpartum BCS must be negative. This would likely cause bias due to part-whole influence to result in genetic correlation estimates between BCS at calving and BCSL that are stronger (positive) than the true genetic correlation. Likewise, bias due to part-whole relationships might result in genetic correlation estimates between postpartum BCS and BCSL that are stronger (negative) than the true genetic correlation.
Some part-whole influence on genetic correlation estimates might have occurred when BCS and BCSL observation were from the same cows, particularly for genetic correlation estimates between BCS at calving and BCSL. The correlation estimates between BCSL and BCS at calving were stronger (negative) in both first and second lactation when estimates were obtained through pedigree linkages only (Table 3). This would seem to indicate that the genetic correlation estimates were positively biased when obtained using observations of BCS at calving and BCSL from the same cows. Genetic correlation estimates between BCSL and postpartum BCS were stronger (negative) in first lactation, but stronger (positive) in second lactation when estimates were obtained through pedigree linkages only.
Despite some potential part-whole bias, there was one pattern that was consistent across all analyses; genetic correlation estimates between postpartum BCS and BCSL were stronger (negative) than estimates between BCS at calving and BCSL. This indicates that selection programs that increase BCSL are likely to do so by lowering postpartum BCS levels more than BCS at calving.
Cows in an experimental herd that have been selected for increased yield have higher dairy form scores (and are thus more angular and thin) than control herd-mates that are bred to maintain a 1964 genetic level for production (Boettcher et al., 1993). However, the selected line has higher incidences of metabolic diseases normally associated with cows that are over-conditioned at calving than the control line (Jones et al., 1994). As cows become genetically thinner, the amount of BCS lost during early lactation is likely to increase at a given level of BCS at calving. Continued selection, whether directly or indirectly, for thinner cows is likely to continue to increase negative energy balance and BCSL in early lactation. Additionally, the target levels for BCS at calving that are recommended to dairy producers may need to reflect genetic trends for BCS.
The genetic and phenotypic correlations between BCSL and production were low to moderately positive. Cows that are genetically inclined to lose more BCS in early lactation tend to have higher yields of milk, fat and protein. Genetic correlations were similar in magnitude to those previously reported (range –0.06 to –0.31) for production and postpartum BCS (Dechow et al., 2001). Waltner et al. (1993), reported that BCSL in the range of 0.5 to 1.5 BCS was associated with higher production. However, Garnsworthy and Jones (1987) reported that thinner cows had higher dry matter intakes, produced a larger proportion of milk directly from food, and produced milk more efficiently than fatter cows that mobilized more body condition. Selection programs that increase yield without increasing levels of BCSL may result in more efficient dairy production than those that do not account for BCSL.
The genetic relationship between BCSL and DFS was unfavorable before and after adjustment for ME milk. The magnitude of the genetic correlations between BCSL and DFS were similar to those reported by Dechow et al. (2001) for postpartum BCS and DFS (range –0.57 to –0.76). Cows genetically inclined to maintain BCS in early lactation and have higher postpartum BCS are inseminated earlier in the lactation. Cows in negative energy balance are reported to have delayed luteal activity and estrus (Butler et al., 1981; de Vries et al., 1999; Harrison et al., 1990). Cows that are genetically inclined to lose more BCS and have low levels of postpartum BCS are subject to more negative energy balance in early lactation, which appears to delay onset of luteal activity and first estrus.
Genetic correlations between BCSL and SPC were positive in first lactation, but negative in second lactation (Table 5). The standard errors for the genetic correlations between BCSL and SPC were high however, ranging from 0.22 to 0.44. Several authors have reported that fertility decreases as BCSL increases (Domecq et al., 1997; Gillund et al., 2001; Loeffler et al., 1999). The effects of DFS on SPC were not accounted for in the genetic analyses. The effects of BCSL on SPC may not be observed when DFS is not considered and may have resulted in inconsistent genetic correlation estimates between BCSL and SPC.
Cows were losing BCS and in negative energy balance until near pregnancy check in this study. Services per conception decreased from nearly four when DFS was 25 to less than two when DFS was 175. Some of the relationship observed between SPC and DFS may be exaggerated by the nature of the data set. If successful inseminations were more likely to be reported by producers than unsuccessful inseminations, SPC are likely to be under-reported and SPC would be expected to decline as DFS increases. However, the trend was strong and likely reflects more than recording inaccuracies. The reduction in SPC as DFS increases likely reflects the effect of negative energy balance on fertility.
The effect of DFS on SPC could have implications for reproductive management. Inseminating a large proportion of cows in the first two months of lactation is likely to lower herd conception rates and increase semen expenditures. However, waiting to inseminate cows when they are likely to be most fertile will increase calving intervals. Moreover, average BCS at the following calving could be higher because of an extended lactation, which is likely to result in greater negative energy balance the following lactation. An alternative may be to use less expensive or young sire semen in early lactation and more expensive semen later in lactation. Additionally, tracking BCS change could help determine which cows are in more severe negative energy balance and therefore candidates for delayed DFS or less expensive semen.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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BCSL has a strong negative correlation with postpartum BCS both genetically and phenotypically, but has a lower heritability than postpartum BCS. Moreover, genetic correlation estimates between BCSL and both production and reproductive performance are similar in magnitude to the genetic correlation estimates between postpartum BCS and performance. Selection for higher postpartum BCS would likely be more efficient in maintaining or improving reproductive performance than selection for reduced BCSL.
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ACKNOWLEDGEMENTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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FOOTNOTES |
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Received for publication January 10, 2002. Accepted for publication June 11, 2002.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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