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Deriving fractional rate of degradation of logistic-exponential (LE) model to evaluate early in vitro fermentation

Published online by Cambridge University Press:  07 January 2013

M. Wang ,
X. Z. Sun ,
S. X. Tang ,
Z. L. Tan  and
D. Pacheco
M. Wang
Affiliation:
Key Laboratory of Agro-ecological Processes in Subtropical Region, Huanjiang Experimental Station of Karst Agro-ecosystem, Institute of Subtropical Agriculture, the Chinese Academy of Sciences, Hunan 410125, P. R. China
X. Z. Sun
Affiliation:
Animal Nutrition & Health, Grasslands Research Centre, AgResearch Limited, Private Bag 11008, Palmerston North, New Zealand
S. X. Tang
Affiliation:
Key Laboratory of Agro-ecological Processes in Subtropical Region, Huanjiang Experimental Station of Karst Agro-ecosystem, Institute of Subtropical Agriculture, the Chinese Academy of Sciences, Hunan 410125, P. R. China
Z. L. Tan*
Affiliation:
Animal Nutrition & Health, Grasslands Research Centre, AgResearch Limited, Private Bag 11008, Palmerston North, New Zealand
D. Pacheco
Affiliation:
Animal Nutrition & Health, Grasslands Research Centre, AgResearch Limited, Private Bag 11008, Palmerston North, New Zealand
*
E-mail: wing_mail@hotmail.com
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Abstract

Water-soluble components of feedstuffs are mainly utilized during the early phase of microbial fermentation, which could be deemed an important determinant of gas production behavior in vitro. Many studies proposed that the fractional rate of degradation (FRD) estimated by fitting gas production curves to mathematical models might be used to characterize the early incubation for in vitro systems. In this study, the mathematical concept of FRD was developed on the basis of the Logistic-Exponential (LE) model, with initial gas volume being zero (LE0). The FRD of the LE0 model exhibits a continuous increase from initial (FRD0) toward final asymptotic value (FRDF) with longer incubation time. The relationships between the FRD and gas production at incubation times 2, 4, 6, 8, 12 and 24 h were compared for four models, in addition to LE0, Generalization of the Mitscherlich (GM), cth order Michaelis–Menten (MM) and Exponential with a discrete LAG (EXPLAG). A total of 94 in vitro gas curves from four subsets with a wide range of feedstuffs from different laboratories and incubation periods were used for model testing. Results indicated that compared with the GM, MM and EXPLAG models, the FRD of LE0 model consistently had stronger correlations with gas production across the four subsets, especially at incubation times 2, 4, 6, 8 and 12 h. Thus, the LE0 model was deemed to provide a better representation of the early fermentation rates. Furthermore, the FRD0 also exhibited strong correlations (P < 0.05) with gas production at early incubation times 2, 4, 6 and 8 h across all four subsets. In summary, the FRD of LE0 model provides an alternative to quantify the rate of early stage incubation, and its initial value could be an important starting parameter of rate.

Keywords

mathematical modelsrate of gas productionlogistic-exponential modelin vitro fermentation
Type
Nutrition
Information
animal , Volume 7 , Issue 6 , June 2013 , pp. 920 - 929
Copyright
Copyright © The Animal Consortium 2012

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References

Calabrò, S, López, S, Piccolo, V, Dijkstra, J, Dhanoa, MS, France, J 2005. Comparative analysis of gas production profiles obtained with buffalo and sheep ruminal fluid as the source of inoculum. Animal Feed Science and Technology 123–124, 51–65.Google Scholar
Chai, WZ, Van Gelder, AH, Cone, JW 2004. Relationship between gas production and starch degradation in feed samples. Animal Feed Science and Technology 114, 195–204.Google Scholar
Colombatto, D, Mould, FL, Bhat, MK, Morgavi, DP, Beauchemin, KA, Owen, E 2003. Influence of fibrolytic enzymes on the hydrolysis and fermentation of pure cellulose and xylan by mixed ruminal microorganisms in vitro. Journal of Animal Science 81, 1040–1050.Google Scholar
Cone, JW, Becker, PM 2012. Fermentation kinetics and production of volatile fatty acids and microbial protein by starchy feedstuffs. Animal Feed Science and Technology 172, 34–41.Google Scholar
Cone, JW, van Gelder, AH 1999. Influence of protein fermentation on gas production profiles. Animal Feed Science and Technology 76, 251–264.Google Scholar
Cone, JW, Van Gelder, AH, Driehuis, F 1997. Description of gas profiles with a three phasic model. Animal Feed Science and Technology 66, 31–45.Google Scholar
Dhanoa, MS, López, S, Dijkstra, J, Davies, DR, Sanderson, R, Williams, BA, Sileshi, Z, France, J 2000. Estimating the extent of degradation of ruminant feeds from a description of their gas production profiles observed in vitro: comparison of models. British Journal of Nutrition 83, 131–142.Google Scholar
Dijkstra, J, Kebreab, E, Bannink, A, France, J, López, S 2005. Application of the gas production technique to feed evaluation systems for ruminants. Animal Feed Science and Technology 123–124, 561–578.Google Scholar
France, J, Dijkstra, J, Dhanoa, MS, Lpez, S, 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.Google Scholar
France, J, Dhanoa, MS, Theodorou, MK, Lister, SJ, Davies, DR, 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.Google Scholar
France, J, López, S, Kebreab, E, Bannink, A, Dhanoa, MS, Dijkstra, J 2005. A general compartmental model for interpreting gas production profiles. Animal Feed Science and Technology 123–124, 473–485.CrossRefGoogle Scholar
Groot, JCJ, Cone, JW, Williams, BA, Debersaques, FMA, 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
Jalilvand, G, Odongo, NE, Lopez, S, Naserian, A, Valizadeh, R, Shahrodi, FE, Kebreab, E, France, J 2008. Effects of different levels of an enzyme mixture on in vitro gas production parameters of contrasting forages. Animal Feed Science and Technology 146, 289–301.CrossRefGoogle Scholar
López, S, France, J, Dhanoa, MS, Mould, F, Dijkstra, J 1999. Comparison of mathematical models to describe disappearance curves obtained using the polyester bag technique for incubating feeds in the rumen. Journal of Animal Science 77, 1875–1888.Google Scholar
López, S, Dhanoa, MS, Dijkstra, J, Bannink, A, Kebreab, E, France, J 2007. Some methodological and analytical considerations regarding application of the gas production technique. Animal Feed Science and Technology 135, 139–156.Google Scholar
Mauricio, RM, Owen, E, Mould, FL, Givens, I, Theodorou, MK, France, J, Davies, DR, Dhanoa, MS 2001. Comparison of bovine rumen liquor and bovine faeces as inoculum for an in vitro gas production technique for evaluating forages. Animal Feed Science and Technology 89, 33–48.Google Scholar
Muetzel, S, Hunt, CL, Tavendale, MH 2011. Brief communication: evaluating rumen fluid from sheep and cattle as inoculum in a newly developed automated in vitro rumen batch culture system. Proceedings of the New Zealand Society of Animal Production 71, 240–242.Google Scholar
Murray, J, Longland, A, Moore-Colyer, M 2006. In vitro fermentation of different ratios of high-temperature dried lucerne and sugar beet pulp incubated with an equine faecal inoculum. Animal Feed Science and Technology 129, 89–98.Google Scholar
Murray, J, Longland, A, Dunnett, C 2008. Effect of yeast supplementation on the in vitro fermentation of high-temperature dried lucerne incubated with an equine faecal inoculum. Animal Feed Science and Technology 146, 149–159.Google Scholar
Oshita, T, Sudo, K, Nonaka, S, Ochiai, K 2008. The efffect of the feed regimen on chewing time, digesta passage rate and particle size distribution in Holstein non-lactating cows fed pasture ad libitum. Livestock Science 113, 243–250.CrossRefGoogle Scholar
Schofield, P, Pitt, RE, Pell, AN 1994. Kinetics of fibre digestion from in vitro gas production. Journal of Animal Science 72, 2980–2991.Google Scholar
Sherrod, PH 1995. NLREG: Nonlinear Regression Analysis Program. Brentwood, TN, USA. Available at http://www.nlreg.com.Google Scholar
Sun, XZ, Hoskin, SO, Muetzel, S, Molano, G, Clark, H 2011. Effect of forage chicory (Cichorium intybus) and Perennial ryegrass (Lolium perenne) on methane emissions in vitro and from sheep. Animal Feed Science and Technology 166–167, 391–397.CrossRefGoogle Scholar
Tang, S, Tan, Z, Zhou, C, Jiang, H, Jiang, Y, Sheng, L 2006. A comparison of in vitro fermentation characteristics of different botanical fractions of mature maize stover. Journal of Animal and Feed Science 15, 505–515.Google Scholar
Tang, SX, Jiang, HL, Zhou, CS, Tan, ZL 2005. Effects of different forage species on in vitro gas production characteristics. Acta Prataculturae Sinica 14, 72–77.Google Scholar
Tang, SX, Tayo, GO, Tan, ZL, Sun, ZH, Wang, M, Ren, GP, Han, XF 2008. Use of in vitro gas production technique to investigate interactions between rice straw, wheat straw, maize stover and alfalfa or clover. Asian–Australian Journal of Animal Science 21, 1278–1285.Google Scholar
Wang, M, Tang, SX, Tan, ZL 2011. Modeling in vitro gas production kinetics: derivation of Logistic-Exponential (LE) equations and comparison of models. Animal Feed Science and Technology 165, 137–150.CrossRefGoogle Scholar