Hoffman P C, Weigel K A, Wernberg R M
Department of Dairy Science, University of Wisconsin, Madison 53706, USA.
J Dairy Sci. 2008 Sep;91(9):3699-709. doi: 10.3168/jds.2007-0644.
Daily pen dry matter intakes (DMI, n = 9,275) were collected over a 28-mo period at the University of Wisconsin's Integrated Dairy Research Facility. Heifers were housed in pens containing 8 Holstein or Holstein x Jersey crossbred heifers/pen. Heifer diets were formulated to energy and protein requirement twice monthly, with feed intake, dietary nutrient density, and ambient temperature recorded daily. Heifers were weighed at 60-d intervals, and mean pen body weights (BW) were estimated for each day between the weigh dates using the interval average daily gain as a regression coefficient. Prediction of heifer DMI was evaluated using the equations of NRC (2001), Quigley et al. (1986), or alternative random effects mixed models or nonlinear exponential models. The effects of breed, BW, temperature and neutral detergent fiber deviation (NDFdv) were considered as independent variables. Holstein and crossbred heifer DMI was predicted with reasonable precision [standard error (SE) < 0.86 kg/d], by the NRC (2001) or Quigley et al. (1986) equations, but heifer DMI was over- or underpredicted for heifers >500 kg, respectively. Improved heifer DMI prediction equations were achieved with exponential models. For Holsteins (SE = 0.71 kg/d), the prediction equation was: DMI (kg/d) = 15.79 x [1 - e((-0.00210 x BW))] - 0.0820 x NDFdv, where NDFdv = (dietary neutral detergent fiber as a % of dry matter) - {22.07 + [0.08714 x BW] - [0.00007383 x (BW)(2)]}. For crossbred heifers (SE = 0.60 kg/d), the prediction equation was: DMI (kg/d) = 13.48 x [1 - e((-0.00271 x BW))] - 0.0824 x NDFdv where NDFdv = (dietary neutral detergent fiber as a % of dry matter) - {23.11 + [0.07968 x BW] - [0.00006252 x (BW)(2)]}. Alternative exponential DMI model equations when dietary neutral detergent fiber is unknown were also developed. The Holstein DMI equation (SE = 0.73 kg/d) was: DMI (kg/d) = 15.36 x [1 - e((-0.00220 x BW))], and the crossbred DMI equation (SE = 0.81 kg/d) was: DMI (kg/d) = 12.91 x [1 - e((-0.00295 x BW))].
在威斯康星大学综合奶牛研究设施中，在28个月的时间里收集了每日干物质摄入量（DMI，n = 9275）。小母牛被饲养在每栏容纳8头荷斯坦或荷斯坦×泽西杂交小母牛的围栏中。小母牛的日粮每月根据能量和蛋白质需求配制两次，每天记录采食量、日粮营养密度和环境温度。每隔60天称一次小母牛体重，并使用间隔平均日增重作为回归系数来估算称重日期之间每一天的平均栏内体重（BW）。使用美国国家研究委员会（NRC，2001）、奎格利等人（1986）的方程，或替代随机效应混合模型或非线性指数模型对小母牛DMI进行预测评估。将品种、体重、温度和中性洗涤纤维偏差（NDFdv）的影响视为自变量。NRC（2001）或奎格利等人（1986）的方程能够以合理的精度预测荷斯坦和杂交小母牛的DMI [标准误差（SE）< 0.86 kg/d]，但对于体重>500 kg的小母牛，其DMI分别被高估或低估。使用指数模型得到了改进的小母牛DMI预测方程。对于荷斯坦牛（SE = 0.71 kg/d），预测方程为：DMI（kg/d）= 15.79 × [1 - e^((-0.00210 × BW))] - 0.0820 × NDFdv，其中NDFdv =（日粮中性洗涤纤维占干物质的百分比）- {22.07 + [0.08714 × BW] - [0.00007383 × (BW)^2]}。对于杂交小母牛（SE = 0.60 kg/d），预测方程为：DMI（kg/d）= 13.48 × [1 - e^((-0.00271 × BW))] - 0.0824 × NDFdv，其中NDFdv =（日粮中性洗涤纤维占干物质的百分比）- {23.11 + [0.07968 × BW] - [0.00006252 × (BW)^2]}。还开发了日粮中性洗涤纤维未知时的替代指数DMI模型方程。荷斯坦牛的DMI方程（SE = 0.73 kg/d）为：DMI（kg/d）= 15.36 × [1 - e^((-0.00220 × BW))]，杂交小母牛的DMI方程（SE = 0.81 kg/d）为：DMI（kg/d）= 12.91 × [1 - e^((-0.00295 × BW))]。
