Shah M A, Murphy M R
Department of Animal Sciences, University of Illinois, Urbana, 61801, USA.
J Dairy Sci. 2006 Jan;89(1):294-306. doi: 10.3168/jds.S0022-0302(06)72094-X.
Inaccurate prediction of dry matter intake (DMI) limits the ability of current models to anticipate the technical and economic consequences of adopting different strategies for production management on individual dairy farms. The objective of the present study was to develop an accurate, robust, and broadly applicable prediction model and to compare it with the current NRC model for dairy cows in early lactation. Among various functions, an exponential model was selected for its best fit to DMI data of dairy cows in early lactation. Daily DMI data (n = 8,547) for 3 groups of Holstein cows (at Illinois, New Hampshire, and Pennsylvania) were used in this study. Cows at Illinois and New Hampshire were fed totally mixed diets for the first 70 d of lactation. At Pennsylvania, data were for the first 63 d postpartum. Data from Illinois cows were used as the developmental dataset, and the other 2 datasets were used for model evaluation and validation. Data for BW, milk yield, and milk composition were only available for Illinois and New Hampshire cows; therefore, only these 2 datasets were used for model comparisons. The exponential model, fitted to the individual cow daily DMI data, explained an average of 74% of the total variation in daily DMI for Illinois data, 49% of the variation for New Hampshire data, 67% of the variation for Pennsylvania data, and 64% of the variation overall. Based on all model selection criteria used in this study, the exponential model for prediction of weekly DMI of individual cows was superior to the current NRC equation. The exponential model explained 85% of the variation in weekly mean DMI compared with 42% for the NRC equation. Compared with the relative prediction error of 6% for the exponential model, that associated with prediction using the NRC equation was 14%. The overall mean square prediction error value for individual cows was 5-fold higher for the NRC equation than for the exponential model (10.4 vs. 2.0 kg2/d2). The consistently accurate and robust prediction of DMI by the exponential model for all data-sets suggested that it could safely be used for predicting DMI in many circumstances.
干物质摄入量（DMI）预测不准确限制了当前模型预测采用不同生产管理策略对个体奶牛场技术和经济后果的能力。本研究的目的是开发一个准确、稳健且广泛适用的预测模型，并将其与当前用于早期泌乳奶牛的NRC模型进行比较。在各种函数中，选择指数模型是因为它最适合早期泌乳奶牛的DMI数据。本研究使用了3组荷斯坦奶牛（伊利诺伊州、新罕布什尔州和宾夕法尼亚州）的每日DMI数据（n = 8547）。伊利诺伊州和新罕布什尔州的奶牛在泌乳的前70天饲喂全混合日粮。在宾夕法尼亚州，数据是产后前63天的。伊利诺伊州奶牛的数据用作开发数据集，其他2个数据集用于模型评估和验证。体重、产奶量和牛奶成分的数据仅适用于伊利诺伊州和新罕布什尔州的奶牛；因此，仅使用这2个数据集进行模型比较。根据个体奶牛每日DMI数据拟合的指数模型，解释了伊利诺伊州数据中每日DMI总变异的74%、新罕布什尔州数据变异的49%、宾夕法尼亚州数据变异的67%以及总体变异的64%。基于本研究中使用的所有模型选择标准，预测个体奶牛每周DMI的指数模型优于当前的NRC方程。指数模型解释了每周平均DMI变异的85%，而NRC方程为42%。与指数模型6%的相对预测误差相比，使用NRC方程预测的相对预测误差为14%。NRC方程对个体奶牛的总体均方预测误差值比指数模型高5倍（10.4对2.0 kg²/d²）。指数模型对所有数据集的DMI进行一致准确且稳健的预测，表明它可以在许多情况下安全地用于预测DMI。
