Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and -0.019 (-0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and -0.022 (-0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.

中文翻译：

---

巴西荷斯坦牛遗传评估中多次泌乳的自回归和随机回归测试日模型

自回归 (AR) 和随机回归 (RR) 模型适用于巴西荷斯坦牛前三个泌乳期的测试日记录，目的是比较它们在国家遗传评估中的效率。数据包括来自 2,322 个牛群的 274,335 头奶牛的 4,142,740 条产奶量 (MY) 和体细胞评分 (SCS) 记录。虽然模型和性状之间的遗传力相似，但使用 AR 的加性遗传方差估计比从 RR 模型获得的高 7.0 (MY) 和 22.2% (SCS)。另一方面，当通过 AR 模型估计时，两个性状的残差方差都较低。对于公牛（有 10 个或更多女儿）和母牛，从 AR 和 RR 模型获得的 EBV 之间的等级相关分别为 0.96 和 0.94（MY）以及 0.97 和 0.95（SCS）。使用 AR 获得的公牛（母牛）的估计年遗传增益为 46.11 (49.50) kg 和 SCS 得分为 -0.019 (-0.025)；而使用 RR 这些值是 47.70 (55.56) kg 和 -0.022 (-0.028) 分数。在两个性状中，AR 的 Akaike 信息标准较低。虽然 AR 模型更加简约，但 RR 模型假设遗传相关性不同于哺乳期内和哺乳期之间的统一。因此，当这些相关性相对较高时，这些模型往往会产生相似的预测；否则，它们的差异会更大，并且 RR 模型在理论上会更完善。RR 模型假设遗传相关性不同于哺乳期内和哺乳期之间的统一。因此，当这些相关性相对较高时，这些模型往往会产生相似的预测；否则，它们的差异会更大，并且 RR 模型在理论上会更完善。RR 模型假设遗传相关性不同于哺乳期内和哺乳期之间的统一。因此，当这些相关性相对较高时，这些模型往往会产生相似的预测；否则，它们的差异会更大，并且 RR 模型在理论上会更完善。