Fuentes-Pila J, DeLorenzo M A, Beede D K, Staples C R, Holter J B
Department of Dairy and Poultry Sciences, University of Florida, Gainesville 32611-0920, USA.
J Dairy Sci. 1996 Sep;79(9):1562-71. doi: 10.3168/jds.S0022-0302(96)76518-9.
The accuracy of seven DMI prediction equations based only on animal factors was evaluated with 11 independent data files. Mean square prediction error was used to compare equation accuracy, which was considered to be unsatisfactory when the square root of the mean square prediction error was greater than +/-20% of the observed mean DMI. Robust intake equations that have a tolerable level of prediction errors for most data files would be less risky for practical use than models that are highly accurate for some data files but highly inaccurate for others. The number of independent data files for which equation accuracy was unsatisfactory was used to measure lack of robustness. No equation evaluated was able to predict individual cow DMI with a prediction error that was consistently lower than +/-20% of the observed mean intake. The most robust equation in this study predicted intake unsatisfactorily for 3 of the 11 evaluation data files. Unsatisfactory accuracy for this equation was mainly due to mean bias.
利用11个独立数据文件评估了仅基于动物因素的7个干物质摄入量(DMI)预测方程的准确性。采用均方预测误差来比较方程的准确性,当均方预测误差的平方根大于观测到的平均DMI的±20%时,则认为准确性不令人满意。对于大多数数据文件而言,具有可容忍预测误差水平的稳健摄入量方程在实际应用中的风险要低于那些对某些数据文件高度准确但对其他数据文件高度不准确的模型。使用方程准确性不令人满意的独立数据文件数量来衡量稳健性的缺乏。所评估的方程中,没有一个能够预测个体奶牛的DMI,其预测误差始终低于观测到的平均摄入量的±20%。本研究中最稳健的方程在11个评估数据文件中的3个中对摄入量的预测不令人满意。该方程准确性不令人满意主要是由于平均偏差。