Comparison of two software programs for fitting one- and two-compartment age-dependent non-linear digestion models for ruminants: empirical data

Abstract

Context The total mass and kinetics of feed particles through the digestive tract affect feed intake, nutrient excretion and emissions by ruminants. Use of models to calculate digesta kinetics parameters will assist managers in mitigating enteric methane emission and producing food more sustainably. Aims We evaluated two software programs for fitting parameters to one- and two-compartment age-dependent digesta kinetic models from faecal-marker concentration datasets. Methods We examined biases (mean differences) and standard deviations (differences) of one-compartment (G2) and two-compartment (G2G1) models with a gamma-2 distribution in the age-dependent compartment when parameterised with two different software programs (R or SAS), using 41 datasets of ytterbium concentrations in faecal samples collected at discrete times. Faecal-marker concentration datasets were fitted to G2 and G2G1 models with each software program. The resulting model parameters, K0, λ or λ1, K2 and τ, were used to calculate the digesta kinetics parameters: particle passage rate, gastrointestinal dry matter fill, faecal dry matter output, gastrointestinal mean retention time and rumen retention time. We evaluated bias and standard deviation for model and digesta kinetic parameters across the entire range of average values, but also within low, medium and high percentile range-of-value subsets (5–35%, 35–65% and 65–95%) between software programs. Key results When datasets were fitted to the G2 model, all converged for both software programs, but when fitted to the G2G1 model by the SAS program, three observations did not converge. Bias and standard deviation of differences between software packages were small, but the G2G1 model produced smaller bias and standard deviation of differences. Bias and standard deviation of differences for digesta kinetics estimates across the percentile groups did not vary linearly for most model estimates and were small relative to the magnitude of the values. Conclusions Model parameters and digesta kinetics estimates derived from R and SAS software programs can be used interchangeably in nutritional modelling. Two-compartment models (G2G1) can be more problematic to fit, but residual mean-square errors are usually smaller. Implications Model parameters from both G2 and G2G1 models can be used to derive unbiased estimates of digesta kinetics from either R or SAS software program.