Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, S-901 83 Umeå, Sweden.
J Dairy Sci. 2010 Dec;93(12):5890-901. doi: 10.3168/jds.2010-3457.
A meta-analysis of studies using the flux/compartmental pool method with indigestible neutral detergent fiber (iNDF) as internal marker was conducted to study the effect of extrinsic characteristics and forage type on particle passage rate (k(p)) in cattle. Further, the k(p) prediction equations in the National Research Council (NRC) and the Cornell Net Carbohydrate and Protein System (CNCPS) were evaluated. Data comprised 172 treatment means from 49 studies conducted in Europe and the United States. In total, 145 diets were fed to dairy cows and 27 to growing cattle. A prerequisite for inclusion of an experiment was that dry matter intake, neutral detergent fiber (NDF), proportion of concentrate in the diet, body weight, and diet chemical composition were determined or could be estimated. Mixed model regression analysis including a random study effect was used to generate prediction equations of k(p) and to investigate the relationships between NRC and CNCPS predictions and observed k(p) of iNDF. Prediction equations were evaluated by regressing residual values on the predicted values. The best-fit model when forage type was not included was k(p) (%/h) = 1.19+0.0879 × NDF intake (g/kg of body weight)+0.792 × proportion of concentrate NDF of total NDF+1.21 × diet iNDF:NDF ratio (adjusted residual mean square error = 0.23%/h). The best general equation accounting for an effect of forage type was as follows: k(p) (%/h) = F+1.54+0.0866 × NDF intake (g/kg of body weight) (adjusted residual mean square error = 0.21%/h), where F is the forage adjustment factor of the intercept. The value of F for grass silage, fresh grass, mixes of alfalfa and corn silage, and dry or ensiled alfalfa as sole forage component were 0.00, -0.91, +0.83, and +0.24, respectively. Relationships between predicted and observed k(p) were y = 0.53(± 0.187)+0.41( ± 0.0373) × predicted k(p) and y = 0.58(± 0.162)+0.46(± 0.0377) × predicted k(p) for the NRC and CNCPS models, respectively. Residual analysis of the NRC and CNCPS models resulted in both significant mean biases (observed--predicted) of -2.40 and -1.70% and linear biases of -0.59 and -0.53, respectively. The results from this meta-analysis suggest that ruminal particulate matter k(p) is affected by forage type in the diet. Further, the evaluation of NRC and CNCPS models showed that passage rate equations developed from marker excretion curves markedly deviated from observed k(p) of iNDF derived using the rumen evacuation technique.
采用不消化中性洗涤纤维（iNDF）作为内标物的通量/隔室池法进行了荟萃分析，以研究外源性特性和饲草类型对牛颗粒通过速率（k(p)）的影响。此外，还评估了美国国家研究委员会（NRC）和康奈尔净碳水化合物和蛋白质系统（CNCPS）的 k(p)预测方程。数据包括来自欧洲和美国的 49 项研究的 172 个处理平均值。总共给 145 个奶牛日粮和 27 个生长牛日粮喂食了饲料。进行实验的一个前提是确定或可以估计干物质采食量、中性洗涤纤维（NDF）、日粮中浓缩物的比例、体重和日粮化学成分。使用包含随机研究效应的混合模型回归分析生成 k(p)的预测方程，并研究 NRC 和 CNCPS 预测值与 iNDF 观察到的 k(p)之间的关系。通过将残差回归到预测值来评估预测方程。当不包括饲草类型时，最佳拟合模型为 k(p)（%/h）=1.19+0.0879×NDF 摄入量（g/kg 体重）+0.792×浓缩物 NDF 占总 NDF 的比例+1.21×日粮 iNDF：NDF 比（调整后的残差均方误差=0.23%/h）。考虑饲草类型影响的最佳通用方程如下：k(p)（%/h）=F+1.54+0.0866×NDF 摄入量（g/kg 体重）（调整后的残差均方误差=0.21%/h），其中 F 是截距的饲草调整因子。对于草青贮料、新鲜草、苜蓿和玉米青贮料混合物以及干草或青贮苜蓿作为单一饲草成分，F 值分别为 0.00、-0.91、+0.83 和+0.24。NRC 和 CNCPS 模型的预测 k(p)与观察到的 k(p)之间的关系分别为 y=0.53(±0.187)+0.41(±0.0373)×预测 k(p)和 y=0.58(±0.162)+0.46(±0.0377)×预测 k(p)。NRC 和 CNCPS 模型的残差分析导致显著的平均偏差（观察值-预测值）分别为-2.40%和-1.70%和线性偏差分别为-0.59%和-0.53%。这项荟萃分析的结果表明，瘤胃颗粒物质 k(p)受日粮中饲草类型的影响。此外，对 NRC 和 CNCPS 模型的评估表明，从标记排泄曲线开发的通过速率方程明显偏离了使用瘤胃排空技术获得的 iNDF 观察到的 k(p)。
