Comparison of two software programs for fitting one- and two-compartment age-dependent non-linear digestion models for ruminants: empirical data
S. A. Gunter
A * , M. S. Gadberry B , K. P. Coffey C and C. A. Moffet A
+ Author Affiliations
- Author Affiliations
A United State Department of Agriculture, Agricultural Research Service, Southern Plains Range Research Station, Woodward, OK 73801-5415, USA.
B Cooperative Extension Service, Division of Agriculture, University of Arkansas, Little Rock, AR 72204-4940, USA.
C Department of Animal Science, Division of Agriculture, University of Arkansas, Fayetteville, AR 73701-3124, USA.
Animal Production Science 62(16) 1630-1638 https://doi.org/10.1071/AN21311
Submitted: 9 June 2021 Accepted: 1 June 2022 Published: 15 July 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing
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.
Keywords: digesta kinetics, dry matter fill, fecal output, intestinal transit time, models, passage rate, ruminal retention time, ruminants.
References
Benchaar C, Pomar C, Chiquette J (2001) Evaluation of dietary strategies to reduce methane production in ruminants: a modelling approach. Canadian Journal of Animal Science 81, 563–574.| Evaluation of dietary strategies to reduce methane production in ruminants: a modelling approach.Crossref | GoogleScholarGoogle Scholar |
Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327, 307–310.
| Statistical methods for assessing agreement between two methods of clinical measurement.Crossref | GoogleScholarGoogle Scholar |
Bunce C (2009) Correlation, agreement, and bland-altman analysis: statistical analysis of method comparison studies. American Journal of Ophthalmology 148, 4–6.
| Correlation, agreement, and bland-altman analysis: statistical analysis of method comparison studies.Crossref | GoogleScholarGoogle Scholar | 19540984PubMed |
Carstensen B, Simpson J, Gurrin LC (2008) Statistical models for assessing agreement in method comparison studies with replicate measurements. The International Journal of Biostatistics 4, 16
| Statistical models for assessing agreement in method comparison studies with replicate measurements.Crossref | GoogleScholarGoogle Scholar |
Cochran RC, Adams DC, Galyean ML, Wallace JD (1987) Examination of methods for estimating rate of passage in grazing steers. Journal of Range Management 40, 105–108.
| Examination of methods for estimating rate of passage in grazing steers.Crossref | GoogleScholarGoogle Scholar |
Coffey KP, Pickett EE, Paterson JA, Hunt CW, Miller SJ (1988) Methods of ytterbium analysis for predicting fecal output and flow-rate constants in cattle. Journal of Range Management 41, 426–429.
| Methods of ytterbium analysis for predicting fecal output and flow-rate constants in cattle.Crossref | GoogleScholarGoogle Scholar |
Dhakal K, Moffet CA, Gunter SA (2018) Effects of marker concentration errors on digesta kinetic parameters. Translational Animal Science 2, S121–S124.
| Effects of marker concentration errors on digesta kinetic parameters.Crossref | GoogleScholarGoogle Scholar | 32704756PubMed |
Dhanoa MS, Siddons RC, France J, Gale DL (1985) A multicompartmental model to describe marker excretion patterns in ruminant faeces. British Journal of Nutrition 53, 663–671.
| A multicompartmental model to describe marker excretion patterns in ruminant faeces.Crossref | GoogleScholarGoogle Scholar | 4063294PubMed |
Draper NR, Smith H (1998) ‘Applied regression analysis.’ (John Wiley & Sons: New York, NY, USA)
Ellis WC, Matis JH, Lascano C (1979) Quantitating ruminal turnover. Federation Proceedings 38, 2702–2706.
Ellis WC, Matis JH, Hill TM, Murphy MR (1994) Methodology for estimating digestion and passage kinetics of forages. In ‘Forage quality, evaluation, and utilization’. (Ed. GC Fahey) pp. 682–756. (American Society of Agronomy, Crop Science Society of America, Soil Science Society of America: Madison, WI, USA)
Elzhov TV, Mullen KM, Spiess A-N, Bolker B. (2016) Package ‘minpack.Lm’ r interface to the levenberg-marquardt nonlinear least-squares algorithm found in minpack, plus support for bounds. Available at ftp://r-project.org/pub/R/web/packages/minpack.lm/minpack.lm.pdf [verified 2 February 2018]
Faichney GJ (2005) Digesta flow. In ‘Quantitive aspents of rumiant digestion and metabolism’. (Eds J Dijkstra, JM Forbes, J France) pp. 49–86. (CABI Publishing: Wallingford, UK)
Gadberry MS, Beck PA, Gunter SA (2004) Forage intake and performance by beef heifers grazing cool-season pasture supplemented with de-oiled rice bran or corn. The Professional Animal Scientist 20, 394–400.
| Forage intake and performance by beef heifers grazing cool-season pasture supplemented with de-oiled rice bran or corn.Crossref | GoogleScholarGoogle Scholar |
Gadberry MS, Beck PA, Kellogg DW, Gunter SA (2005) Digestion characteristics and growth of steers fed a corn-grain based supplement compared to a de-oiled rice bran plus cottonseed supplement with or without extrusion processing. Animal Feed Science and Technology 118, 267–277.
| Digestion characteristics and growth of steers fed a corn-grain based supplement compared to a de-oiled rice bran plus cottonseed supplement with or without extrusion processing.Crossref | GoogleScholarGoogle Scholar |
Galyean ML (1993) Technical note: an algebraic method for calculating fecal output from a pulse dose of an external marker. Journal of Animal Science 71, 3466–3469.
| Technical note: an algebraic method for calculating fecal output from a pulse dose of an external marker.Crossref | GoogleScholarGoogle Scholar | 8294301PubMed |
Galyean ML, Gunter SA (2016) Predicting forage intake in extensive grazing systems. Journal of Animal Science 94, 26–43.
| Predicting forage intake in extensive grazing systems.Crossref | GoogleScholarGoogle Scholar |
Goopy JP, Korir D, Pelster D, Ali AIM, Wassie SE, Schlecht E, Dickhoefer U, Merbold L, Butterbach-Bahl K (2020) Severe below-maintenance feed intake increases methane yield from enteric fermentation in cattle. British Journal of Nutrition 123, 1239–1246.
| Severe below-maintenance feed intake increases methane yield from enteric fermentation in cattle.Crossref | GoogleScholarGoogle Scholar | 32209141PubMed |
Gunter SA, Moffet CA, Gadberry MS, Coffey KP (2020) External marker concentrations overtime in cattle feces after pulse dosed with ytterbium. Mendeley Data V1.
| Crossref |
Hammond KJ, Pacheco D, Burke JL, Koolaard JP, Muetzel S, Waghorn GC (2014) The effects of fresh forages and feed intake level on digesta kinetics and enteric methane emissions from sheep. Animal Feed Science and Technology 193, 32–43.
| The effects of fresh forages and feed intake level on digesta kinetics and enteric methane emissions from sheep.Crossref | GoogleScholarGoogle Scholar |
Hart SP, Polan CE (1984) Simultaneous extraction and determination of ytterbium and cobalt ethylenediaminetetraacetate complex in feces. Journal of Dairy Science 67, 888–892.
| Simultaneous extraction and determination of ytterbium and cobalt ethylenediaminetetraacetate complex in feces.Crossref | GoogleScholarGoogle Scholar |
Herd RM, Velazco JI, Arthur PF, Hegarty RS (2016) Proxies to adjust methane production rate of beef cattle when the quantity of feed consumed is unknown. Animal Production Science 56, 231–237.
| Proxies to adjust methane production rate of beef cattle when the quantity of feed consumed is unknown.Crossref | GoogleScholarGoogle Scholar |
Hristov AN, Melgar A (2020) Short communication: relationship of dry matter intake with enteric methane emission measured with the greenfeed system in dairy cows receiving a diet without or with 3-nitrooxypropanol. Animal 14, S484–S490.
| Short communication: relationship of dry matter intake with enteric methane emission measured with the greenfeed system in dairy cows receiving a diet without or with 3-nitrooxypropanol.Crossref | GoogleScholarGoogle Scholar | 32720629PubMed |
Huhtanen P, Kukkonen U (1995) Comparison of methods, markers, sampling sites and models for estimating digesta passage kinetics in cattle fed at two levels of intake. Animal Feed Science and Technology 52, 141–158.
| Comparison of methods, markers, sampling sites and models for estimating digesta passage kinetics in cattle fed at two levels of intake.Crossref | GoogleScholarGoogle Scholar |
Huhtanen P, Ramin M, Cabezas-Garcia EH (2016) Effects of ruminal digesta retention time on methane emissions: a modelling approach. Animal Production Science 56, 501–506.
| Effects of ruminal digesta retention time on methane emissions: a modelling approach.Crossref | GoogleScholarGoogle Scholar |
IPCC (2006) ‘IPCC guidelines for national greenhouse gas inventories. Vol. 4. Agriculture, forestry and other land use.’ (Intergovernmental Panel on Climate Change: Geneva, Switzerland) Available at http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html [verified 16 April 2021]
Krysl LJ, McCollum FT, Galyean ML (1985) Estimation of fecal output and particulate passage rate with a pulse dose of ytterbium-labeled forage. Journal of Range Management 38, 180–182.
| Estimation of fecal output and particulate passage rate with a pulse dose of ytterbium-labeled forage.Crossref | GoogleScholarGoogle Scholar |
Krysl LJ, Galyean ML, Estell RE, Sowell BF (1988) Estimating digestibility and faecal output in lambs using internal and external markers. The Journal of Agricultural Science 111, 19–25.
| Estimating digestibility and faecal output in lambs using internal and external markers.Crossref | GoogleScholarGoogle Scholar |
Ludbrook J (2010) Confidence in Altman–Bland plots: a critical review of the method of differences. Clinical and Experimental Pharmacology and Physiology 37, 143–149.
| Confidence in Altman–Bland plots: a critical review of the method of differences.Crossref | GoogleScholarGoogle Scholar | 19719745PubMed |
Luginbuhl JM, Pond KR, Burns JC (1994) Whole-tract digesta kinetics and comparison of techniques for the estimation of fecal output in steers fed coastal bermudagrass hay at four levels of intake. Journal of Animal Science 72, 201–211.
| Whole-tract digesta kinetics and comparison of techniques for the estimation of fecal output in steers fed coastal bermudagrass hay at four levels of intake.Crossref | GoogleScholarGoogle Scholar | 8138490PubMed |
Matis JM (1972) 332. Note: Gamma time-dependency in Blaxter’s compartmental model. Biometrics 28, 597–602.
| 332. Note: Gamma time-dependency in Blaxter’s compartmental model.Crossref | GoogleScholarGoogle Scholar |
Moffet CA, Gunter SA (2020a) Comparing two software programs for fitting nonlinear, one- and two-compartment age-dependent digestion models: a monte carlo analysis. Livestock Science 240, 104153
| Comparing two software programs for fitting nonlinear, one- and two-compartment age-dependent digestion models: a monte carlo analysis.Crossref | GoogleScholarGoogle Scholar |
Moffet CA, Gunter SA (2020b) Synthetic fecal marker concentration datasets. Mendeley Data V2.
| Crossref |
Moore JA, Pond KR, Poore MH, Goodwin TG (1992) Influence of model and marker on digesta kinetic estimates for sheep. Journal of Animal Science 70, 3528–3540.
| Influence of model and marker on digesta kinetic estimates for sheep.Crossref | GoogleScholarGoogle Scholar | 1459916PubMed |
Oreskes N, Shrader-Frechette K, Belitz K (1994) Verification, validation, and confirmation of numerical models in the earth sciences. Science 263, 641–646.
| Verification, validation, and confirmation of numerical models in the earth sciences.Crossref | GoogleScholarGoogle Scholar | 17747657PubMed |
Pond KR, Ellis WC, Matis JH, Ferreiro HM, Sutton JD (1988) Compartment models for estimating attributes of digesta flow in cattle. British Journal of Nutrition 60, 571–595.
| Compartment models for estimating attributes of digesta flow in cattle.Crossref | GoogleScholarGoogle Scholar | 3219325PubMed |
Poore MH, Moore JA, Eck TP, Swingle RS (1991) Influence of passage model, sampling site, and marker dosing time on passage of rare earth-labeled grain through Holstein cows. Journal of Animal Science 69, 2646–2654.
| Influence of passage model, sampling site, and marker dosing time on passage of rare earth-labeled grain through Holstein cows.Crossref | GoogleScholarGoogle Scholar | 1885377PubMed |
Quiroz RA, Pond KR, Tolley EA, Johnson WL (1988) Selection among nonlinear models for rate of passage studies in ruminants. Journal of Animal Science 66, 2977–2986.
| Selection among nonlinear models for rate of passage studies in ruminants.Crossref | GoogleScholarGoogle Scholar | 3225248PubMed |
R Core Team (2020) R: A language and environment for statistical computing. The R Foundation, Vienna, Austria. Available at http://www.r-project.org/. [Verified 28 June 2022]
Tedeschi LO (2006) Assessment of the adequacy of mathematical models. Agricultural Systems 89, 225–247.
| Assessment of the adequacy of mathematical models.Crossref | GoogleScholarGoogle Scholar |
Teeter RG, Owens FN, Mader TL (1984) Ytterbium chloride as a marker for particulate matter in the rumen. Journal of Animal Science 58, 465–473.
| Ytterbium chloride as a marker for particulate matter in the rumen.Crossref | GoogleScholarGoogle Scholar |
Van Soest PJ, Robertson JB, Lewis BA (1991) Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science 74, 3583–3597.
| Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition.Crossref | GoogleScholarGoogle Scholar | 1660498PubMed |
Vieira RAM, Tedeschi LO, Cannas A (2008) A generalized compartmental model to estimate the fibre mass in the ruminoreticulum: 2. Integrating digestion and passage. Journal of Theoretical Biology 255, 357–368.
| A generalized compartmental model to estimate the fibre mass in the ruminoreticulum: 2. Integrating digestion and passage.Crossref | GoogleScholarGoogle Scholar |
