مقایسه برازش برخی از مدل های ریاضی به منظور توصیف کینتیک تخمیر شکمبه ای بر اساس آزمون تولید گاز برای علوفه یونجه

نوع مقاله : مقاله پژوهشی

نویسنده

استادیار گروه علوم دامی، دانشکده کشاورزی، دانشگاه بو علی سینا همدان

چکیده

در این تحقیق از مدل­های ریاضی برای بررسی کینتیک تخمیر شکمبه­ای علوفه یونجه استفاده شد. این مدل­ها شامل مدل نمایی (EXP)، میکائیلیس-­منتن (MIC)، میچرلینگ (MIT)، ویبول (WEB)، کورکمز-­اوکاردس (KOR) و فرانس (FRC) بودند. آزمون تولید گاز در 4 دوره جداگانه انجام شد. تعداد 3 عدد سرنگ (3 تکرار) حاوی نمونه خوراک برای هر دوره در نظر گرفته شد و حجم گاز تولید شده در هر دوره در زمان­های مختلف انکوباسیون (144 ساعت) به وسیله این مدل­ها برازش شد. از آماره­های میانگین مربعات خطا (MSE)، ضریب تعیین (R2) و میانگین درصد خطا (MPE) برای نکویی ­برازش مدل­ها استفاده شد. از آزمون­های دوربین-واتسون، شاپیرو-ویلک، معیار اطلاعات بیزی (BIC)، معیار اطلاعات آکائیک (AIC) و فاکتور صحت (AF) برای انتخاب بهترین مدل استفاده شد. نتایج نشان داد مقدار MSE در مدل­های FRC (852/0) و MIC (917/0) نسبت به مدل EXP (437/7) کمتر بود (05/0P<)، اما مقدار R2 در مدل­های FRC و MIC (به ترتیب 997/0 و 997/0) در مقایسه با مدل EXP (973/0) بیشتر بود (05/0P<). آزمون شاپیرو-ویلک نشان داد به­غیر از مدل EXP، مقدار خطا در همه مدل­ها دارای توزیع نرمال بود. مقادیر کمتر BIC،AIC  و AF در مدل FRC (به ترتیب 47/6- ، 18/6- و 20/2) و در مدل MIC (به ترتیب 32/4- ، 98/3- و 40/2) نشان داد که این مدل­ها دارای نکویی ­برازش بهتری در مقایسه با سایر مدل­ها بودند. به طور کلی مدل­های FRC و MIC کینتیک تخمیر شکمبه­ای علوفه یونجه را با دقت بیشتری برآورد کردند. لذا می­توان برای توصیف پروفیل تولید گاز از مدل­های فوق به­جای مدل EXP استفاده شود.

کلیدواژه‌ها

عنوان مقاله [English]

Comparison of fitting of some mathematical models to describe the ruminal fermentation kinetics according to gas production technique for alfalfa hay

نویسنده [English]

  • Kh. Zaboli
Assistant professor, Animal Science Department, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
چکیده [English]

In this study, the mathematical models were used for evaluation of ruminal fermentation kinetic of alfalfa hay. These models included exponential (EXP), Michaelis-Menten (MIC), Mitscherling (MIT), Weibull (WEB), Korkmaz-Uckardes (KOR) and France (FRC). The in vitro gas production was carried out in 4 separate periods. Three syringes containing feed samples (3 replicates) were considered for each period and the volume of gas produced in each period at different incubation times (144 hours) was fitted for these models. Mean Square Error (MSE), coefficient determination (R2) and Mean Percentage Error (MPE) were used for models goodness of fit. Durbin-Watson and Shapiro-Wilk tests, Bayesian Information Criterion (BIC), Akaike’s Information Criterion (AIC) and Accuracy Factor (AF) were used for selection of the best model. The results showed that MSE in FRC (0.852) and MIC (0.917) models was lower than that of EXP (7.437) model (P<0.05). However, R2 in FRC and MIC models (0.997 and 0.997, respectively) was significantly higher than that of EXP (0.973) model (P<0.05). Shapiro-Wilk test showed that all models, except EXP model, had normal distributions of the error values. Lower values of BIC, AIC and AF showed that FRC (-6.47, -6.18 and 2.20, respectively) and MIC (-4.32, -3.98 and 2.40, respectively) models had better goodness of fit compared to other models. Generally, the FRC and MIC models estimated ruminal fermentation kinetic of alfalfa hay more accurately. So, these models may be used to describe gas production profiles instead of EXP model.

کلیدواژه‌ها [English]

  • Gas production technique
  • Mathematical model
  • goodness of fit
  • Alfalfa

مراجع

AOAC. 1995. Official Methods of Analysis, 16th ed. Association of Official Analytical Chemists, Arlington, V. A.
Dhanoa M. S., Lopez S., Dijkstra J., Davies D. R., Sanderson R., Williams A. B., Zileshi Z. and 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.
Draper N. R. and Smith H. 1981. Applied Regression Analysis. Wiley, New York, USA.
France J., Dhanoa M. S., Theodorou M. K., Lister S. J., Davies D. R. 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.
France J., Dijkstra J., Dhanoa M. S., 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.
France J., Lopez S., Kebreab E., Bannink A., Dhanoa M. S. and Dijkstra J. 2005. A general compartmental model for interpreting gas production profiles. Animal Feed Science and Technology, 123-124: 473-485.
Groot J. C. J., Cone J. W., Williams B. A., Debersaques F. M. A. and Lantinga E. A. 1996. Multiphasic analysis of gas production kinetics for in vitro fermentation of ruminant feeds. Animal Feed Science and Technology, 64: 77–89.
He Z. X., Zhao Y. L., McAllister T. A. and Yang W. Z. 2016. Effect of in vitro techniques and exogenous feed enzymes on feed digestion. Animal Feed Science and Technology, 213: 148–152.
Huhtanen P., Seppälä A., Ahvenjärvi S. and Rinne M. 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.
Korkmaz M. and Uckades F. 2014. An alternative robust model for in situ degradation studies “Korkmaz-Uckardes”. Iranian Journal of Applied Animal Science, 4(1): 45-51.
Korkmaz M., Uckades F. and Kaygisiz A. 2011. Comparison 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.
López S., France J., Dhanoa M. S., Mould F. and 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.
López S., Prieto M., Dijkstra J., Dhanoa M. S. and France J. 2004. Statistical evaluation of mathematical models for microbial growth. International Journal of Food Microbiology, 96: 289–300.
Menke K. H. and Steingass H. 1988. Estimation of the energetic feed value obtained from chemical analysis in vitro gas production using rumen fluid. Animal Research and Development, 28: 7-55.
Menke K. H., Raab L., Salewski A., Steingass H., Fritz D. and Schneider W. 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.
Motulsky H. J. and Ransnas L. A. 1987. Fitting curves to data using nonlinear regression: a practical and non-mathematical review. FASEB Journal, 1: 365–374.
NRC. 2007. Nutrient Requirements of Small Ruminants: Sheep, Goats, Cervids, and New World Camelids. Natl. Acad. Press, Washington, DC.
Ørskov E. R. 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 and Technology, 92: 499–503.
Peripolli V., Prates E. R., Barcellos J. O. J., McManus C. M., Wilbert C. A., BracciniNeto J., Camargo C. M. and Lopes R. B. 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.
Pineiro G., Perelman S., Guerschman J. P. and Paruelo J. M. 2008. How to evaluate models: observed vs. predicted or predicted vs. observed? Ecological Modelling, 216: 316–322.
Sahin M., Uckardes F., Canbolat O., Kamalak A. and Atalay A. I. 2011. Estimation of partial gas production times of some feedstuffs used in ruminant nutrition. Kafkas Üniversitesi Veteriner Fakültesi Dergisi Journal, 17: 731-734.
SAS. 1999. The SAS System for Windows. Release 8.0.1. SAS Institute Inc, Cary, USA.
Tedeschi L. O., Schofield P. and Pell A. N. 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, MG, Brazil
Theodorou M. K., Williams B. A., Dhanoa M. S., McAllan A. B. and France J. 1994. A simple gas production method using a pressure transducer to determine the fermentation kinetics of ruminant feeds. Animal Feed Science and Technology, 48: 185–197.
Ucardes F. and Efe E. 2014. Investigation on the usability of some mathematical models in in vitro gas production techniques. Slovak Journal of Animal Science, 47 (3): 172-179.
Uckardes F. 2013. A modified Mitscherlich model and its degradation kinetics equations. Archiv Tierzucht, 56 (101): 1005-1013.
Uckardes F., Korkmaz M. and Ocal P. 2013. Comparison of models and estimation of missing parameters of some mathematical models related to in situ dry matter degradation. Journal of Animal and Plant Sciences, 23: 999-1007.
Uckardes F., Korkmaz M. and Ocal P. 2013. Comparison of models and estimation of missing parameters of some mathematical models related to in situ dry matter degradation. The Journal of Animal and Plant Sciences, 23(4): 999-1007.
Van Soest P. J., Robertson J. B. and Lewis B. A. 1991. Methods for dietary fiber, neutral detergent fiber and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science, 74: 3583–3597.
Wang M., Tang S. X. and Tan Z. L. 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.
West S. E. 1999. Guidance for data quality assessment. EPA Company, Washington. 1999, p. 4-6.
Zwitering M. H., Jongenburger I., Rombouts F. M. and Van’tRiet K. 1990. Modeling of the bacterial growth curve. Applied and Environmental Microbiology, 56(6): 1875-1881.
تحقیقات تولیدات دامی
دوره 5، شماره 3
آذر 1395
صفحه 35-47

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