Norman H D, VanRaden P M, Wright J R, Clay J S
Animal Improvement Programs Laboratory, USDA, Beltsville, MD 20705-2350, USA.
J Dairy Sci. 1999 Feb;82(2):438-44. doi: 10.3168/jds.S0022-0302(99)75250-1.
A method with best prediction properties that condenses information from all test days into measures of lactation yield and persistency has been proposed as a possible replacement for the test interval method and projection factors. The proposed method uses previously established correlations between individual test days and includes inversion of a matrix for each lactation. Milk weights that were representative of monthly, a.m.-p.m., and trimonthly test plans were examined to compare the accuracy of best prediction and test interval methods for estimating lactation yield. Individual milk weights or daily yields of 658 Canadian cows in 17 herds were selected to correspond to test intervals for 100,000 US cows. For a.m.-p.m. testing, the initial milk weight that was credited was selected randomly from the a.m. or p.m. milking and was alternated thereafter. Trimonthly credits were from one of the first three designated test day weights, selected randomly, and each third designated test weight thereafter. Correlations between 305-d actual lactation yield and lactation estimates by the test interval method were 0.97, 0.96, and 0.93 for monthly, a.m.-p.m., and trimonthly testing, respectively. Corresponding correlations for the best prediction method were 0.97, 0.97, and 0.93. Standard deviations of differences between estimated and 305-d actual yields for monthly, a.m.-p.m., and trimonthly testing were 373, 400, and 546 kg, respectively, for best prediction regressed on herd mean, which was a reduction in estimation error of 4, 6, and 10% over the test interval method. The advantage of best prediction was moderate if two milk weights were recorded monthly and was larger if testing was less frequent. Advantages also were found for fat and protein yields estimated by multitrait best prediction for records with reduced component sampling.
一种具有最佳预测特性的方法已被提出，该方法将所有测试日的信息浓缩为泌乳量和持久性的度量，有可能替代测试间隔法和预测因子。所提出的方法利用了先前建立的各个测试日之间的相关性，并且针对每个泌乳期求矩阵的逆。研究了代表月度、上午-下午和每三个月测试计划的牛奶重量，以比较最佳预测法和测试间隔法在估计泌乳量方面的准确性。选取了17个牛群中658头加拿大奶牛的个体牛奶重量或日产量，使其对应于100,000头美国奶牛的测试间隔。对于上午-下午测试，记入的初始牛奶重量从上午或下午挤奶中随机选择，此后交替进行。每三个月的记入重量来自前三个指定测试日重量中的一个，随机选择，此后为每第三个指定测试重量。对于月度、上午-下午和每三个月测试，测试间隔法估计的305天实际泌乳量与泌乳量估计值之间的相关性分别为0.97、0.96和0.93。最佳预测法的相应相关性为0.97、0.97和0.93。对于以牛群平均值为回归变量的最佳预测法，月度、上午-下午和每三个月测试的估计产量与305天实际产量之间差异的标准差分别为373、400和546千克，与测试间隔法相比，估计误差降低了4%、6%和10%。如果每月记录两次牛奶重量，最佳预测法的优势适中；如果测试频率较低，优势则更大。对于成分抽样减少的记录，通过多性状最佳预测法估计的脂肪和蛋白质产量也发现了优势。
