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Estimating the extent of degradation of ruminant feeds from a description of their gas production profiles observed in vitro: comparison of models

Published online by Cambridge University Press:  09 March 2007

M. S. Dhanoa ,
S. Lopez ,
J. Dijkstra ,
D. R. Davies ,
R. Sanderson ,
B. A. Williams ,
Z. Sileshi  and
J. France
M. S. Dhanoa
Affiliation:
Institute of Grassland and Environmental Research, Plas Gogerddan, Aberystwyth SY23 3EB, UK
S. Lopez
Affiliation:
Department of Animal ProductionUniversity of Leon, 24007 Leon, Spain
J. Dijkstra
Affiliation:
Animal Nutrition Group, Wageningen Institute of Animal Sciences, Wageningen Agricultural University, Marijkeweg 40, 6709 PG Wageningen, The Netherlands
D. R. Davies
Affiliation:
Institute of Grassland and Environmental Research, Plas Gogerddan, Aberystwyth SY23 3EB, UK
R. Sanderson
Affiliation:
Institute of Grassland and Environmental Research, Plas Gogerddan, Aberystwyth SY23 3EB, UK
B. A. Williams
Affiliation:
Animal Nutrition Group, Wageningen Institute of Animal Sciences, Wageningen Agricultural University, Marijkeweg 40, 6709 PG Wageningen, The Netherlands
Z. Sileshi
Affiliation:
Institute of Agricultural Research, PO Box 2003, Ethiopia
J. France*
Affiliation:
The University of Reading, Department of Agriculture, PO Box 236, Earley Gate, Reading RG6 6AT, UK
*
*Corresponding author: Professor J. France, fax +44 (0)118 935 2421, email j.france@reading.ac.uk
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Abstract

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An evaluation of general models that describe gas production profiles is presented. The models are derived from first principles by considering a simple three-pool scheme and permit the extent of ruminal degradation to be calculated, as described in the companion paper. The models evaluated were the generalized Mitscherlich, simple Mitscherlich, generalized Michaelis–Menten, simple Michaelis–Menten, Gompertz, and logistic. Five sets of gas production data consisting of 216 curves, obtained using a wide range of feeds (including straw, hay, silage, grain and various byproducts), were analysed to study the performance of these gas production models. Application of the non-sigmoidal models (simple Mitscherlich and Michaelis–Menten) to the data resulted in convergence problems and these models were found to be inadequate in many cases. Based on results of a pairwise comparison between models (variance ratio test), ranking of residual mean squares, lack-of-fit test, and of analyses of residuals, the generalized Mitscherlich and the generalized Michaelis–Menten models seemed particularly suited because of their flexibility to encompass sigmoidal and non-sigmoidal shapes of gas production profiles, whether symmetrical or not.

Keywords

RumenMathematical modelsGas productionFeed degradation
Type
Research Article
Information
British Journal of Nutrition , Volume 83 , Issue 2 , February 2000 , pp. 131 - 142
Copyright
Copyright © The Nutrition Society 2000

References

Agricultural and Food Research Council (1992) –. AFRC Technical Committee on responses to nutrients. Report no. 9. Nutritive requirements of ruminant animals: protein. Nutrition Abstracts and Reviews, Series B 62, 787–835.Google Scholar
Beuvink, JMW and Kogut, J (1993) Modelling gas production kinetics of grass silages incubated with buffered ruminal fluid. Journal of Animal Science 71, 1041–1046.CrossRefGoogle ScholarPubMed
Beuvink, JMW and Spoelstra, SF (1992) Interactions between substrate, fermentation end-products, buffering systems and gas production upon fermentation of different carbohydrates by mixed rumen microorganisms. in vitro Applied Microbiology and Biotechnology 37, 505–509.Google Scholar
Bibby, J & Toutenberg, H (1977) Prediction and Improved Estimation in Linear Models. London: John Wiley & Sons.Google Scholar
Blummel, M and Ørskov, ER (1993) Comparison of in vitro gas production and nylon-bag degradability of roughage in predicting feed intake in cattle. Animal Feed Science and Technology 40, 109–119.CrossRefGoogle Scholar
Blummel, M, Steingaß, H and Becker, K (1997) The relationship between in vitro gas production, in vitro microbial biomass yield and 15N incorporation and its implications for the prediction of voluntary feed intake of roughages. British Journal of Nutrition 77, 911–921.CrossRefGoogle ScholarPubMed
Cone, JW Van Gelder, AH, Visscher, GJW and Oudshoorn, L (1996) Influence of rumen fluid and substrate concentration on fermentation kinetics measured with a fully automated time related gas production apparatus. Animal Feed Science and Technology 61, 113–128.CrossRefGoogle Scholar
Dijkstra, J, France, J and Davies, DR (1998) Different mathematical approaches to estimating microbial protein supply in ruminants. Journal of Dairy Science 81, 3370–3384.CrossRefGoogle ScholarPubMed
Dijkstra, JFrance JLopez, S & Dhanoa, MS (1999) Impact of variation in yield of gas during incubation on simulated gas production and extent of ruminal degradation. In Proceedings of the British Society of Animal Science, p. 38. Edinburgh: British Society of Animal Science.Google Scholar
Draper, NR & Smith, H (1981) Applied Regression Analysis. New York, NY: John Wiley & Sons.Google Scholar
France, J, Dhanoa, MS, Theodorou, MK, Lister, SJ, Davies, DR and Isac, D (1993) A model to interpret gas accumulation profiles associated with in vitro degradation of ruminant feeds Journal of Theoretical Biology 163, 99–111.CrossRefGoogle Scholar
France, J, Dijkstra, J, Dhanoa, MS, Lopez, S and Bannink, A (2000) Estimating the extent of degradation of ruminant feeds from a description of their gas production profiles observed in vitro: derivation of models and other mathematical considerations. British Journal of Nutrition 83, 143–150.CrossRefGoogle ScholarPubMed
France, JDijkstra Lopez, JS & Dhanoa, MS (1998) Models for interpreting in vitro gas production profiles from ruminant feeds. In In Vitro Techniques for Measuring Nutrient Supply to Ruminants, BSAS Publication no. 22, pp. 79–80 [Deaville, ER, Owen, E, Adesogan, AT, Rymer, C, Huntington, JA and Lawrence, TLJ, editors]. Edinburgh: British Society of Animal Science.Google Scholar
Genstat 5 Committee (1987) Genstat 5 Manual. Oxford: Clarenden Press.Google Scholar
Groot, JCJ, Cone, JW, Williams, BA, Debersaques, FMA and Lantinga, EA (1996) Multiphasic analysis of gas production kinetics for in vitro fermentation of ruminant feeds. Animal Feed Science and Technology 64, 77–89.CrossRefGoogle Scholar
Hungate, RE (1966) The Rumen and its Microbes. New York, NY: Academic Press Inc.Google Scholar
Kaske, M and von Engelhardt, W (1990) British Journal of Nutrition. 63, 457–465.CrossRefGoogle Scholar
Khazaal, K, Boza, J and Ørskov ER (1994) Assessment of phenolics-related antinutritive effects in Mediterranean browse: a comparison between the use of the in vitro gas production technique with or without insoluble polyvinylpolypyrrolidone or nylon bag. Animal Feed Science and Technology 49, 133–149.CrossRefGoogle Scholar
Khazaal, K, Dentinho, MT, Ribeiro, JM and Ørskov ER (1993) A comparison of gas production during incubation with rumen contents in vitro and nylon bag degradability as predictors of the apparent digestibility in vivo and the voluntary intake of hays. Animal Production 57, 105–112.Google Scholar
Lopez, S, Carro, MD, Gonzalez, JS and Ovejero, FJ (1998) Comparison of different in vitro and in situ methods to estimate the extent and rate of degradation of hays in the rumen Animal Feed Science and Technology 73, 99–113.CrossRefGoogle Scholar
Motulsky, HJ and Ransnas, LA (1987) Fitting curves to data using nonlinear regression: a practical and nonmathematical review FASEB Journal 1, 365–374.Google Scholar
Murphy, MR, Baldwin, RL and Koong, LJ (1982) Estimation of stoichiometric parameters for rumen fermentation of roughage and concentrate diets .Journal of Animal Science 55, 411–421.CrossRefGoogle ScholarPubMed
Nocek, JE and Kohn, RA (1987) Initial particle form and size on change in functional specific gravity of alfalfa and timothy hay. Journal of Dairy Science 70, 1850–1863.Google Scholar
Ørskov ER and McDonald I (1979) The estimation of protein degradability in the rumen from incubation measurements weighted according to rates of passage. Journal of Agricultural Science, Cambridge 92, 499–503.CrossRefGoogle Scholar
Pell, AN and Schofield, P (1993) Computerized monitoring of gas production to measure forage digestion in vitro. Journal of Dairy Science 76, 1063–1073.CrossRefGoogle ScholarPubMed
Pirt, SJ (1975) Principles of Microbe and Cell Cultivation. Oxford: Blackwell Scientific Publications.Google Scholar
Ross, GJS (1987) MLP Maximum Likelihood Program, version 3.08. Oxford: Numerical Algorithms Group.Google Scholar
Russell, JB, Sniffen, CJ and Van Soest, PJ (1983) Effect of carbohydrate limitation on degradation and utilization of casein by mixed rumen bacteria Journal of Dairy Science 66, 763–775.CrossRefGoogle ScholarPubMed
Schofield, P, Pitt, RE and Pell, AN (1994) Kinetics of fibre digestion from in vitro gas production Journal of Animal Science 72, 2980–2991.CrossRefGoogle ScholarPubMed
Sileshi, Z (1995) Development of a simple in vitro gas production technique using a pressure transducer, to estimate rate of digestion of some Ethiopian forages. PhD Thesis, University of Reading.Google Scholar
Sileshi, Z, Owen, E, Dhanoa, MS and Theodorou, MK (1996) Prediction of in situ rumen dry matter disappearance of Ethiopian forages from an in vitro gas production technique using a pressure transducer, chemical analysis or in vitro digestibility .Animal Feed Science and Technology 61, 73–87.CrossRefGoogle Scholar
Tamminga, S Van Straalen, WM, Subnel, APJ, Meijer, RGM, Steg, A, Wever, CJG and Blok, MC (1994) The Dutch protein evaluation system: the DVB/OEB-system. Livestock Production Science 40, 139–155.CrossRefGoogle Scholar
Tamminga, S Van Vuuren, AM, Van der Koelen, CJ, Ketelaar, RS and Van der Togt, PL (1990) Ruminal behaviour of structural carbohydrates, non-structural carbohydrates and crude protein from concentrate ingredients in dairy cows. Netherlands Journal of Agricultural Science 38, 513–526.CrossRefGoogle Scholar
Tamminga, S & Williams, BA (1998) In vitro techniques as tools to predict nutrient supply in ruminants. In In Vitro Techniques for Measuring Nutrient Supply to Ruminants, BSAS Publication no. 22, pp. 1–11 [Deaville, ER, Owen, E, Adesogan, AT, Rymer, C, Huntington, JA and Lawrence, TLJ, editors]. Edinburgh: British Society of Animal Science.Google Scholar
Theodorou, MK, Williams, BA, Dhanoa, MS, McAllan, AB and France, J (1994) A simple gas production method using a pressure transducer to determine the fermentation kinetics of ruminant feedstuffs. Animal Feed Science and Technology 48, 185–197.CrossRefGoogle Scholar
Tilley, JMA and Terry, RA (1963) A two stage technique for the in vitro digestion of forage crops. Journal of the British Grassland Society 18, 104–111.CrossRefGoogle Scholar
Tukey, JW (1977) Exploratory Data Analysis. Reading, MA: Addison-Wesley.Google Scholar
Williams, BA, Chuzaemi, S Soebarinoto, Van Bruchem, J, Boer, H and Tamminga, S (1996) A comparison of ten rice-straw varieties grown at two different altitudes during a wet and a dry season, using the in vitro cumulative gas production technique Animal Feed Science and Technology 57, 183–194.CrossRefGoogle Scholar
Williams, BA, Van der Poel, AFB, Boer, H and Tamminga, S (1995) The use of cumulative gas production to determine the effect of steam explosion on the fermentability of two substrates with different cell wall quality Journal of the Science of Food and Agriculture 69, 33–39.CrossRefGoogle Scholar
Zwietering, MH, Jongenburger, I, Rombouts, FM and Van 't Riet, K (1990) Modelling of the bacterial growth curve .Applied and Environmental Microbiology 56, 1875–1881.CrossRefGoogle ScholarPubMed