Short communication: Effect of preadjusting test-day yields for stage ...

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SHorT CoMMUNICATIoN: TeST-DAY YIelDS AND STAGe oF PreGNANCY Table 2. Average daily heritabilities on the diagonal, genetic correlations below the diagonal, and permanent environment correlations above the diagonal (unadjusted data set) Item Milk (kg) First parity Second parity Third parity Fat (kg) Protein (kg) SCS Milk (kg) the differences between the genetic parameters of data sets adjusted and unadjusted for pregnancy effect were not significantly different from 0. Assuming similar results for the preadjustment of phenotypes for other Canadian breeds, variance components may not need to be re-estimated before using records preadjusted for pregnancy in the CTDM. Preadjusting for pregnancy had little effect on bull EBV: the EBV of 99% of bulls did not change by more than 50 kg for milk yield (EBV SD = 630 kg), and 2 kg for fat (EBV SD = 24 kg) and protein yield (EBV SD = 20 kg). However, larger changes occurred for lactation persistency EBV, for which the relative EBV of 3.5% of bulls changed by 2 points (EBV SD = 5 points). When an adjustment is not made for pregnancy, pregnant Fat (kg) Protein (kg) SCS Milk (kg) Fat (kg) 2273 Protein (kg) SCS First parity Milk (kg) 0.41 0.91 0.96 −0.27 0.59 0.50 0.57 −0.08 0.46 0.34 0.42 0.05 Fat (kg) 0.84 0.35 0.92 −0.29 0.51 0.54 0.53 −0.12 0.43 0.42 0.43 −0.01 Protein (kg) 0.93 0.86 0.39 −0.24 0.60 0.55 0.63 −0.09 0.48 0.40 0.49 0.02 SCS −0.04 −0.06 −0.04 0.31 −0.17 −0.17 −0.16 0.44 −0.19 −0.19 −0.18 0.38 Second parity Milk (kg) 0.73 0.60 0.66 −0.09 0.45 0.92 0.96 −0.33 0.57 0.50 0.55 −0.11 Fat (kg) 0.58 0.70 0.58 −0.07 0.87 0.40 0.94 −0.38 0.55 0.59 0.57 −0.16 Protein (kg) 0.66 0.63 0.70 −0.06 0.93 0.89 0.43 −0.33 0.57 0.53 0.59 −0.13 SCS −0.10 −0.09 −0.06 0.49 −0.34 −0.33 −0.32 0.40 −0.20 −0.20 −0.18 0.54 Third parity Milk (kg) 0.56 0.46 0.51 −0.02 0.70 0.60 0.65 −0.18 0.43 0.92 0.96 −0.38 Fat (kg) 0.39 0.53 0.41 −0.04 0.57 0.70 0.60 −0.18 0.86 0.38 0.93 −0.43 Protein (kg) 0.49 0.47 0.55 −0.03 0.62 0.59 0.68 −0.17 0.93 0.87 0.42 −0.35 SCS −0.03 −0.08 −0.03 0.28 −0.26 −0.30 −0.27 0.55 −0.28 −0.37 −0.27 0.39 cows would appear to be genetically less persistent. It is therefore logical to see a change in lactation persistency EBV after preadjusting records for pregnancy effect. Overall, larger changes were observed for cow EBV; in particular, a significant decrease of cow indexes for top elite cows with large days open that have been flushed extensively for embryo transfer. As expected, cows that did not have large days open showed an increase in EBV for production traits. Leclerc et al. (2008) compared a 2-step procedure (preadjustment, then genetic evaluation) with a 1-step genetic evaluation including all factors in the model. Preadjustment was carried out for effects related to the shape of the lactation curve, including gestation effect using 4-knot regression splines with knots at 100, 150, Table 3. Average daily heritabilities on the diagonal, genetic correlations below the diagonal, and permanent environment correlations above the diagonal (preadjusted data set) Item Milk (kg) First parity Second parity Third parity Fat (kg) Protein (kg) SCS Milk (kg) Fat (kg) Protein (kg) SCS Milk (kg) Fat (kg) Protein (kg) SCS First parity Milk (kg) 0.42 0.91 0.95 −0.29 0.54 0.46 0.53 −0.09 0.45 0.35 0.43 0.03 Fat (kg) 0.83 0.36 0.92 −0.31 0.45 0.49 0.48 −0.12 0.42 0.43 0.42 −0.02 Protein (kg) 0.92 0.87 0.39 −0.27 0.54 0.51 0.59 −0.10 0.47 0.42 0.49 −0.01 SCS −0.03 −0.05 −0.04 0.30 −0.16 −0.16 −0.15 0.43 −0.18 −0.19 −0.18 0.36 Second parity Milk (kg) 0.74 0.59 0.66 −0.09 0.44 0.92 0.96 −0.37 0.55 0.49 0.53 −0.14 Fat (kg) 0.59 0.69 0.58 −0.08 0.86 0.39 0.94 −0.40 0.53 0.57 0.55 −0.16 Protein (kg) 0.67 0.63 0.70 −0.08 0.93 0.89 0.41 −0.36 0.55 0.52 0.58 −0.14 SCS −0.10 −0.10 −0.06 0.50 −0.29 −0.31 −0.29 0.40 −0.18 −0.18 −0.16 0.52 Third parity Milk (kg) 0.56 0.46 0.51 −0.06 0.68 0.59 0.63 −0.20 0.43 0.92 0.96 −0.37 Fat (kg) 0.38 0.53 0.40 −0.09 0.53 0.68 0.56 −0.23 0.84 0.38 0.93 −0.41 Protein (kg) 0.47 0.46 0.53 −0.08 0.60 0.59 0.66 −0.21 0.92 0.86 0.42 −0.33 SCS −0.02 −0.10 −0.02 0.31 −0.20 −0.28 −0.23 0.56 −0.30 −0.40 −0.30 0.40 Journal of Dairy Science Vol. 92 No. 5, 2009
2274 200, and 265 d pregnant. Solutions for random effects were similar between 1- and 2-step procedures, with correlations >0.99. The Interbull guidelines (Interbull, 2001) suggest that using preadjusted records for genetic evaluations should be well justified. Determination of pregnancy by Loker et al. (2009) was not always accurate. For cows with lactation still in progress, conception was assumed using date of last insemination, which was not necessarily correct. Sometimes, insemination records were not available, which would lead to further error when assuming dates of conception and pregnancy status. Bohmanova et al. (2008) separated cows into classes depending on the availability of insemination data. Because insemination date and calving date were not always known, pregnancy status was more accurate for some classes than for others. In the future, preadjustment for pregnancy should be performed using estimates for stage of pregnancy obtained after eliminating records that have erroneous pregnancy information. Conversely, if pregnancy effect was accounted for in the genetic evaluation model, there would be a large number of records with erroneous pregnancy data that would result in inaccurate corrections. Readers should note that there would still be animals for which pregnancy status is uncertain when performing genetic evaluation of records preadjusted for pregnancy. However, because adjustment factors would be accurately estimated based on data with more certain pregnancy status information, the effects of errors in determining pregnancy status for genetic evaluation will be limited compared with using all data to adjust for pregnancy. Results from this study show that preadjusting data for pregnancy did not yield relevant changes in variance component estimates, thus suggesting that preadjusting test-day records could be a feasible solution to account Journal of Dairy Science Vol. 92 No. 5, 2009 loKer eT Al. Table 4. Differences between estimated parameters from preadjusted data set with parameters estimated from unadjusted data set (average daily heritabilities on the diagonal, genetic correlations below the diagonal and permanent environment correlations above the diagonal) Item Milk (kg) First parity Second parity Third parity Fat (kg) Protein (kg) SCS Milk (kg) Fat (kg) Protein (kg) SCS Milk (kg) Fat (kg) Protein (kg) SCS First parity Milk (kg) 0.011 −0.004 −0.007 −0.022 −0.051 −0.041 −0.042 −0.011 −0.001 0.014 0.006 −0.018 Fat (kg) −0.004 0.006 −0.004 −0.022 −0.057 −0.049 −0.050 0.005 −0.009 0.006 −0.005 −0.009 Protein (kg) −0.001 0.005 0.003 −0.028 −0.057 −0.046 −0.043 −0.010 −0.002 0.017 0.007 −0.031 SCS 0.011 0.010 0.006 −0.007 0.005 0.006 0.009 −0.007 0.011 0.007 0.001 −0.016 Second parity Milk (kg) 0.013 −0.008 −0.005 0.003 −0.010 0.004 −0.003 −0.039 −0.020 −0.010 −0.014 −0.023 Fat (kg) 0.011 −0.001 0.002 −0.004 −0.005 −0.007 −0.001 −0.015 −0.020 −0.015 −0.015 −0.001 Protein (kg) 0.015 0.001 −0.002 −0.013 −0.003 0.002 −0.014 −0.024 −0.021 −0.010 −0.007 −0.011 SCS 0.006 −0.012 0.001 0.007 0.053 0.021 0.033 0.008 0.022 0.029 0.018 −0.019 Third parity Milk (kg) 0.004 0.006 −0.001 −0.045 −0.020 −0.008 −0.019 −0.024 −0.006 0.000 −0.007 0.016 Fat (kg) −0.012 0.000 −0.015 −0.050 −0.043 −0.015 −0.036 −0.044 −0.012 −0.002 −0.002 0.024 Protein (kg) −0.014 −0.004 −0.019 −0.056 −0.024 −0.006 −0.020 −0.038 −0.010 −0.007 −0.007 0.021 SCS 0.004 −0.012 0.009 0.031 0.057 0.018 0.045 0.009 −0.019 −0.032 −0.026 0.014 for pregnancy in the CTDM without changing the current model and programs. Re-estimation of pregnancy effects is required after elimination of records for cows whose pregnancy status is not certain. Accounting for pregnancy effect removed some bias from breeding value estimations. Evidence of this is seen in the results of this study, which showed that elite cows used for embryo transfer (cows that had longer days open) experienced a decline in EBV and other cows with shorter days open experienced an increase in EBV when the data set was adjusted for pregnancy. This is expected, because these elite cows lactate without experiencing the negative environmental effect of pregnancy within the first 305 d of lactation. Meanwhile, the negative effect of pregnancy is added back onto records of other, nonelite cows. Therefore, elite cows flushed for embryo transfer do not appear as genetically superior to other cows compared with when pregnancy is not accounted for. Aside from this difference, preadjusting for pregnancy did not have much of an effect on Ayrshire EBV. Therefore, preadjustment of test-day records for the effect of pregnancy before genetic evaluations are performed may improve the estimation of breeding values for dairy cattle, and may be applicable for use in the CTDM without having to change the current model and programs. Similar studies should be performed on other breeds, using preadjustment factors calculated after the removal of data for animals with uncertain pregnancy status. aCKnOWLeDGmentS The authors acknowledge the DairyGen Council of Canadian Dairy Network, and NSERC of Canada for funding this project. Authors are also grateful to the 2 anonymous reviewers for their helpful comments.
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