The Accuracy of some Models to Estimate the Coefficients of Gas Production Test in Corn Silage

پژوهشهای علوم دامی ایران

Article 5, Volume 12, Issue 4 - Serial Number 44, March 2021, Pages 467-479   

Document Type: Ruminant Nutrition

DOI: 10.22067/ijasr.v13i2.80666

Authors

Khalil Zaboli^* ; sara Kalvandi; Mostafa Maleki

Department, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

Abstract

Introduction[1]In vitro gas production technique is one of the methods used for evaluating ruminal fermentation kinetic of feedstuffs. In this method, the volume of gas produced during the incubation is presented as a curve. The mathematical description of gas production profile is performed by fitting data set to a nonlinear model. Recently, several non-linear models have been developed to estimate gas production profile however, some of these models are not accurate enough. Therefore, the aim of this study was to investigate the accuracy of some nonlinear models for predicting ruminal fermentation kinetic of a forage feed.
Materials and methods In this experiment, corn silages (samples on 0, 30 and 60 days after ensiling) were used as fermentation substrates.  Dry matter and chemical composition (organic matter, crude protein, NDF and ADF) of the samples were determined using standard methods. The rumen fluid was obtained from three fistulated rams before the morning feeding. The collected ruminal fluids were pooled and transferred into a flask to the laboratory. The rumen fluid was filtered through four layers cheesecloth, flushed continuously with CO₂ and maintained at 39^oC before incubation. The rumen fluid was then mixed with buffered mineral solution at the ratio of 1:2 (V/V). Gas production technique was completed in three separate runs on three different days (each run lasted 6 days). In each run, the samples were incubated in triplicate and two vials (without the substrate) were considered as the blanks. The volume of gas produced was measured at 0, 2, 4, 6, 8, 12, 16, 20, 24, 48, 72, 96,120 and 144 hours of incubation. The prediction of the gas volume at different times of incubation was compared by four nonlinear models and results were expressed in ml per 200 mg of DM incubated. The selected models (experimental treatments) included Exponential (EXP), Fitzhugh (FZH), logistic (LOG) and Gompertz (GOM). The goodness of fit of the models were evaluated using mean square error (MSE), coefficient of determination (R²), residual mean absolute deviation (RMAD) and mean percentage error (MPE). In addition, Durbin-Watson test (DW), run test and linear regression analysis (between observed and predicted values of the gas volume at different incubation times) were used to assess the accuracy of the models in fitting the data. The estimated ruminal fermentation parameters (the asymptotic gas volume and gas production rate) and goodness of fit parameters obtained from each model (MSE, R², RMAD and MPE statistics) were analyzed using completely randomized design.
Results The studied models had no difference in terms of predicting asymptotic gas volume (A) on 0, 30 and 60 days after ensiling and the value of parameter A predicted by the models were in the range of 98.37 (for GOM model on day 0) to 76.09 (for LOG model on day 60) ml per 200 mg DM. The EXP and LOG models had the highest and lowest MSE and R² values, respectively, indicating their lower accuracy compared with GOM and FZH models. The RMAD value was lowest in GOM and FZH models (2.591 and 2.879, respectively) and was highest in EXP model (3.807). The RMAD value is used as an indicator for evaluating the goodness of fit of models. the lower values of RMAD (closer to zero), represents a better ability of the model in fitting data. Based on these results, GOM and FZH models had a higher accuracy than EXP model in fitting data. The MPE value in the EXP model (5.527) was significantly higher than the other models (p < 0.05). In other words, the predicted values (the volume of gas produced at different times of incubation) by the EXP model were lower than the observed values (it was underestimated). Based on Durbin Watson (DW) test results, the DW statistics in the EXP, FZH, GOM and LOG models were 0.392, 0.691, 0.705 and 0.675, respectively, indicating that EXP and GOM models had the lowest and highest accuracy, respectively, in predicting the rumen fermentation kinetic of corn silage. According to the run test, all the curves in EXP model had the lowest run (3 ≥) implying a poor performance of EXP model in predicting the results. The linear regression between the observed versus predicted values (regression parameters) showed a significant difference between intercept with 0; and slope with 1 in all the studied models (p < 0.05). However, based on the goodness of fit parameters obtained from the linear regression, FZH and GOM models had a better prediction of the gas production profile.
Conclusion The EXP model had lower accuracy in predicting the rumen fermentation kinetic of corn silage compared with the other studied models. It is recommended that other nonlinear models be used in addition to the EXP model for investigating the ruminal fermentation kinetics of corn silage.

Keywords

Goodness of fit; In vitro method; Nonlinear models; Ruminal fermentation kinetic

References

AOAC. 1995. Official Methods of Analysis, 16th ed. Association of Official Analytical Chemists, Arlington, VA.

2- Beuvink, J. M., and J. Kogut. 1993. Modeling gas production kinetics of grass silages incubated with buffered ruminal fluid. Journal of Animal Science, 71(4):1041-1046.

3- Dhanoa, M. S., S. Lopez, J. Dijkstra, D. R. Davies, R. Sanderson, A. B. Williams, Z. Zileshi, and J. France. 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.

4- Draper, N. R., and H. Smith. 1981. Applied regression analysis. Wiley, New York, USA.

5- Fedorak, P. M., and S. E. Hrudey. 1983. A Simple apparatus for measuring gas production by methanogenic cultuvesin serum bottles. Environmental Technology Letters, 4(10): 425- 432.

6- He, Z. X., Y. L. Zhao, T. A. McAllister, and W. Z. Yang. 2016. Effect of in vitro techniques and exogenous feed enzymes on feed digestion. Animal Feed Science and Technology, 213: 148–152.

7- Huhtanen, P., A. Seppälä, S. Ahvenjärvi, and M. Rinne. 2008. Prediction of in vivo neutral detergent fiber digestibility and digestion rate of potentially digestible neutral detergent fiber: comparison of models. Journal of Animal Science, 86: 2657–2669.

8- Kamalak, A., O. Canbolat, Y. Gurbuz, and O. Ozay. 2005. Comparison of in vitro gas production technique with in situ nylon bag technique to estimate dry matter degradation. Czech Journal of Animal Science, 50 (2): 60–67.

9- Korkmaz, M., and F. Uckades. 2014. An alternative robust model for in situ degradation studies “Korkmaz-Uckardes”. Iranian Journal of Applied Animal Science, 4(1): 45-51.

10- Korkmaz, M., F. Uckades, and A. Kaygisiz. 2011. Comparision of wood, gaines, parabolic, hayashi, dhanno and polynomial models for lactation season curve of Simmental cows. Journal of Animal and Plant Sciences, 3: 448-458.

11- López, S., J. France, M. S. Dhanoa, F. Mould, and J. Dijkstra. 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.

12- López, S., M. Prieto, J. Dijkstra, M. S. Dhanoa, and J. France. 2004. Statistical evaluation of mathematical models for microbial growth. International Journal of Food Microbiology, 96: 289- 300.

13- Menke, K. H., and H. Steingass. 1988. Estimation of the energetic feed value obtainedfrom chemical analysis in vitro gas production using rumen fluid. Animal Research and Development, 28: 7-55.

14- Menke, K. H., L. Raab, A. Salewski, H. Steingass, D. Fritz, and W. Schneider. 1979. The estimation of the digestibility and metabolizable energy content of ruminant feeding stuffs from the gas production when they are incubated with rumen liquor in vitro. Journal of Agricultural Science, 93: 217-222.

15- NRC. 2007. Nutrient Requirements of Small Ruminants: Sheep, goats, cervids, and New World camelids. National Academies Press, Washington, DC.

16- Peripolli, V., E. R. Prates, J. O. J. Barcellos, C. M. McManus, C. A. Wilbert, J. BracciniNeto, C. M. Camargo, and R. B. Lopes. 2014. Models for gas production adjustment in ruminant diets containing crude glycerol. Livestock Research for Rural Development 26 (2), from http://www.lrrd.org/lrrd26/2/peri26028.htm.

17- Pineiro, G., S. Perelman, J. P. Guerschman, and J. M. Paruelo. 2008. How to evaluate models: observed vs. predicted or predicted vs. observed? Ecological Modelling, 216: 316–322.

18- Pitt, R. E., T. L. Cross, A. N. Pell, P. Schofield, and P. H. Doane. 1999. Use of in vitro gas production models in ruminal Kinetics. Mathematical Biosciences, 159(2):145-163.

19- Rabbani, H. R. 2012. Study on silage characteristics of two varieties of amaranth forage (amaranthus hypochondriacus) and its comparison with corn silage. Thesis submitted for Master of Science in field of animal science, Department of Animal Science, Bu-Ali Sina University. (In Persian)

20- Sahin, M., F. Uckardes, O. Canbolat, A. Kamalak, and A. I. Atalay. 2011. Estimation of Partial Gas Production Times of Some Feedstuffs Used in Ruminant Nutrition. Kafkas Üniversitesi Veteriner Fakültesi Dergisi Journal, 17: 731-734.

21- SAS, 1999. The SAS system for windows. Release 8.0.1. SAS Institutue Inc, Cary, USA.

22- Schofield, P., R. E. Pitt, and A. N. Pell. 1994. Kinetics of fibre digestion from in vitro gas production. Journal of Animal Science, 72: 2980-2991.

23- Seker, E. 2002. The determination of the energy values of some ruminant feeds by using digestibility trial and gas test. Revue de Medecine Veterinaire, 153(5): 323-328.

24- Tedeschi, L. O., P. Schofield, and A. N. Pell. 2008. Determining feed quality for ruminants using in vitro gas production technique. 1. Building an anaerobic fermentation chamber. In: The 4th Workshop on Modeling in Ruminant Nutrition: Application of the gas production technique, Juiz de Fora, Brazil

25- Tilley, J. M. A., and R. A. Terry. 1963. A two-stage technique for the in vitro digestion of forage crops. Journal of the British Grassland Society, 18: 104-11.

26- Van-Soest, P. J., J. B. Robertson, and B. A. Lewis. 1991. Methods for dietary fiber, neutral detergent fiber and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science, 74: 3583–3597.

27- Wang, M., S. X. Tang, and Z. L. Tan. 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.

28- Wang, M., X. Z. Sun, S. X. tang, Z. L. Tan, and D. Pacheco. 2013. Deriving fractional rate of degradation of logistic-exponential (LE) model to evaluate early in vitro fermentation. Animal, 7(6): 920-929.

29- Zaboli, Kh. 2016. Comparison of fitting of some mathematical models to describe the ruminal fermentation kinetics according to gas production technique for Alfalfa hay. Animal Production Research, 5(3): 35-47. (In Persian)

30- Zwitering, M. H., I. Jongenburger, F. M. Rombouts, and K. Van’tRiet. 1990. Modeling of the bacterial growth curve. Applied and Environmental Microbiology, 56(6):1875-1881.