Using compartmental modeling of digesta kinetics is a valuable tool to ruminant nutritionists assessing and quantifying the site and extent of digestion. To increase the value of one- (G2) and two-compartment (G2G1), age-dependent models, we characterized the repeatability and agreement of 2 software systems for fitting these models. We constructed replicated datasets of fecal marker concentrations over time by sampling 81 synthetic concentration profiles representing 81 animal and diet combinations. Datasets contained fecal marker concentrations for 15 nominal times after dosing (0, 9, 12, 15, 18, 24, 32, 40, 48, 60, 72, 84, 96, 108, and 120 h). Datasets were constructed by adding random errors to samples from hypothetical true marker concentration profiles. Errors included sampling time and marker concentration measurement. The resulting fecal marker concentration datasets were fit to G2 and G2G1 models with programs written for 2 software systems (R and SAS). The resulting model parameters, K₀, λ or λ₁, K₂, and τ, were used to calculate particle passage rate, gastrointestinal DM fill, fecal DM output, gastrointestinal mean retention time, and rumen retention time. We evaluated the repeatability of each software and the agreement between software packages. When fitting the 8,100 datasets to the G2 model, all converged for both software. When fitting the same datasets to a G2G1 model, however, 369 did not converge for SAS and 1 did not converge for R. Non-convergence can be a significant problem when an experiment has minimal experimental units. The R software produced more repeatable model parameter estimates than SAS, but the ratios of repeatability to mean values were generally less than 10%. Bias and SD of differences between software packages were small, however G2G1 models produced smaller bias and SD of differences than the G2 models. Bias and SD for derived digestion parameters between models and software packages were also small. Again the G2G1 model had smaller biases and SD of differences than the G2 model. Repeatability for derived digestion parameters were better with R than with SAS, but the mean differences were small. The G2G1 model produced more repeatable results than the G2 model, but differences between software were small for the G2G1 model.

使用反演消化动力学的分区模型是反刍动物营养学家评估和量化消化部位和程度的有价值的工具。为了增加一室（G2）和二室（G2G1）年龄相关模型的价值，我们对2种适合这些模型的软件系统的可重复性和一致性进行了表征。我们通过采样代表81种动物和饮食组合的81种合成浓度曲线，构建了随时间变化的粪便标记物浓度的重复数据集。数据集包含在给药后15个标称时间的粪便标志物浓度（0、9、12、15、18、24、32、40、48、60、72、84、96、108和120 h）。通过从假设的真实标记物浓度曲线向样品中添加随机误差来构建数据集。错误包括采样时间和标记物浓度测量。通过为2个软件系统（R和SAS）编写的程序，将所得粪便标记物浓度数据集拟合到G2和G2G1模型。结果模型参数，ķ ₀，λ或λ ₁，ķ ₂，和τ用来计算颗粒通过率，胃肠道DM填充，粪便DM输出，胃肠道平均保留时间和瘤胃保留时间。我们评估了每个软件的可重复性以及软件包之间的协议。将8100个数据集拟合到G2模型时，这两个软件的所有数据都已收敛。但是，当将相同的数据集拟合到G2G1模型时，对于SAS不收敛369，对于R不收敛1。当实验的实验单元最少时，不收敛可能是一个严重的问题。与SAS相比，R软件产生的模型参数估计值更具可重复性，但是重复性与平均值的比率通常小于10％。软件包之间差异的偏差和SD很小，但是G2G1模型产生的偏差和SD差异小于G2模型。模型和软件包之间得出的消化参数的偏差和SD也很小。同样，G2G1模型比G2模型具有更小的偏差和SD差异。使用R得出的消化参数的可重复性比使用SAS更好，但是平均差异很小。G2G1模型比G2模型产生了更多的可重复结果，但是对于G2G1模型，软件之间的差异很小。