Cole J B, Null D J, Vanraden P M
Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350, USA.
J Dairy Sci. 2009 Apr;92(4):1796-810. doi: 10.3168/jds.2007-0976.
Lactation records of any reasonable length now can be processed with the selection index method known as best prediction (BP). Previous prediction programs were limited to the 305-d standard used since 1935. Best prediction was implemented in 1998 to calculate lactation records in USDA genetic evaluations, replacing the test interval method used since 1969 to calculate lactation records. Best prediction is more complex but also more accurate, particularly when testing is less frequent. Programs were reorganized to output better graphics, give users simpler access to options, and provide additional output, such as BP of daily yields. Test-day data for 6 breeds were extracted from the national dairy database, and lactation lengths were required to be > or =500 d (Ayrshire, Milking Shorthorn) or > or =800 d (all others). Average yield and SD at any day in milk (DIM) were estimated by fitting 3-parameter Wood's curves (milk, fat, protein) and 4-parameter exponential functions (somatic cell score) to means and SD of 15- (< or =300 DIM) and 30-d (>300 DIM) intervals. Correlations among TD yields were estimated using an autoregressive matrix to account for biological changes and an identity matrix to model daily measurement error. Autoregressive parameters (r) were estimated separately for first (r = 0.998) and later parities (r = 0.995). These r values were slightly larger than previous estimates due to the inclusion of the identity matrix. Correlations between traits were modified so that correlations between somatic cell score and other traits may be nonzero. The new lactation curves and correlation functions were validated by extracting TD data from the national database, estimating 305-d yields using the original and new programs, and correlating those results. Daily BP of yield were validated using daily milk weights from on-farm meters in university research herds. Correlations ranged from 0.900 to 0.988 for 305-d milk yield. High correlations ranged from 0.844 to 0.988 for daily yields, although correlations were as low as 0.015 on d 1 of lactation, which may be due to calving-related disorders that are not accounted for by BP. Correlations between 305-d yield calculated using 50-d intervals from 50 to 250 DIM and 305-yield calculated using all TD to 500 DIM increased as TD data accumulated. Many cows can profitably produce for >305 DIM, and the revised program provides a flexible tool to model these records.
现在，任何合理时长的泌乳记录都可以使用被称为最佳预测（BP）的选择指数法进行处理。以前的预测程序仅限于自1935年以来使用的305天标准。最佳预测于1998年实施，用于美国农业部的遗传评估中计算泌乳记录，取代了自1969年以来用于计算泌乳记录的测试间隔法。最佳预测更为复杂，但也更准确，尤其是在测试频率较低时。程序进行了重组，以输出更好的图表，让用户更便捷地访问选项，并提供额外的输出，如日产奶量的最佳预测值。从国家奶牛数据库中提取了6个品种的测试日数据，要求泌乳期长度≥500天（艾尔夏牛、乳用短角牛）或≥800天（其他所有品种）。通过将三参数伍德曲线（牛奶、脂肪、蛋白质）和四参数指数函数（体细胞评分）拟合到15天（≤300天泌乳天数）和30天（＞300天泌乳天数）间隔的均值和标准差，来估计任何泌乳天数（DIM）的平均产量和标准差。使用自回归矩阵来估计测试日产量之间的相关性，以考虑生物学变化，并使用单位矩阵来模拟每日测量误差。分别对第一胎（r = 0.998）和后续胎次（r = 0.995）估计自回归参数（r）。由于纳入了单位矩阵，这些r值略大于先前的估计值。对性状之间的相关性进行了修正，以便体细胞评分与其他性状之间的相关性可能不为零。通过从国家数据库中提取测试日数据，使用原始程序和新程序估计305天产量，并将这些结果进行关联，从而验证了新的泌乳曲线和相关函数。使用大学研究牛群中农场计量器的每日牛奶重量对日产奶量的最佳预测值进行了验证。305天牛奶产量的相关性在0.900至0.988之间。日产奶量的高相关性在0.844至0.988之间，尽管在泌乳第1天相关性低至0.015，这可能是由于最佳预测未考虑到的与产犊相关的疾病。使用从50至250天泌乳天数的50天间隔计算的305天产量与使用所有测试日至500天泌乳天数计算的305天产量之间的相关性随着测试日数据的积累而增加。许多奶牛在超过305天的泌乳期内仍能盈利生产，修订后的程序为模拟这些记录提供了一个灵活的工具。
