Application of artificial neural networks and multiple linear regression for predicting asymptotic gas production of agricultural by-products

Articles in Press

Document Type : Research Article (Regular Paper)

Authors

1 Department of Agricultural Engineering, National University of Skills (NUS), Tehran, Iran

2 Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute. P.O. Box 31485498, Tehran, Iran

3 Department of Agriculture, Payame Noor University, Tehran, Iran, P. O. Box 19395-3697. Tehran, Iran

4 Department of Animal Science, University of California, Davis, One Shields Avenue, Davis, CA 95616

Abstract

This research explored the correlation between the chemical composition and asymptotic in vitro gas production (AGP) of diverse agricultural by-products, intending to develop predictive models for AGP using advanced computational methods. The research employed two complementary analytical approaches: artificial neural networks (ANN) and multiple linear regression (MLR), to assess their efficacy in forecasting AGP based on compositional parameters. Two datasets were utilized: a training dataset compiled from previously published literature and a testing dataset comprising experimentally derived chemical profiles and AGP measurements of selected by-products. Following the determination of chemical constituents (e.g., neutral detergent fiber [NDF], acid detergent fiber [ADF], organic matter [OM], and crude protein [CP]) and AGP values, the datasets were merged and subjected to multivariate cluster analysis. This analysis revealed two statistically distinct clusters (A and B), with intra-group similarity thresholds exceeding 80% for Cluster A and 90% for Cluster B. The study focused on Cluster A, which encompassed the selected by-products, for subsequent Pearson correlation and predictive modeling. Key findings included significant inverse relationships between AGP and fiber components (NDF: r=−0.65; ADF: r=−0.72), whereas positive correlations emerged with OM (r=0.58) and CP (r=0.49). Comparative model performance demonstrated ANN’s superiority (r²=0.78, RMSE=5.39) over MLR (r²=0.24, RMSE=18.36), highlighting its potential for accurate AGP prediction in agricultural by-products.

Keywords

Main Subjects

References

Adebayo, R.A., Moyo, M., Gueguim Kana, E.B., Nsahlai, I.V.,  2020. The use of artificial neural networks for modelling rumen fill. Canadian Journal of Animal Science 101, 427-437.
Aderao, G.N., Sahoo, A., Bhatt, R.S., Kumawat, P.K, Soni, L., 2018.  In vitro rumen fermentation kinetics, metabolite production, methane and substrate degradability of polyphenol rich plant leaves and their component complete feed blocks. Animal Feed Science and Technology 60, 26; 10.1186/s40781-018-0184-6.
Akinfemi, A., Adua, M.M., Adu, O.A., 2012. Evaluation of nutritive values of tropical feed sources and by-products using in vitro gas production technique in ruminant animals. Emirates Journal of Food and Agriculture 24, 348-353.
AOAC, 1990. Official methods of analysis. 15th ed. Association of Official Analytical Chemists, Arlington, USA.
Baffa, D.F., Oliveira, T.S., Fernandes, A.M., Camilo, M.G., Silva, I.N., Meirelles Júnior, J.R., Aniceto, E.S., 2023. Evaluation of Associative Effects of In Vitro gas production and fermentation profile caused by variation in ruminant diet constituents. Methane 2, 344-360.
Beatriz E.B, Pérez-Márquez, S., Scarpino van Cleef, F.O., Silva Ovani, V., Costa, W.S., Lima, P.M.T., Louvandini, H., Abdalla, A.L., 2023. In vitro degradability and methane production from by-Products fed to ruminants. Agronomy 13, 1043; 10.3390/ agronomy13041043.
Blummel, M., Ørskov, E.R., 1993. Comparison of in vitro gas production and nylon bag degradability of roughages in predicting feed intake in cattle. Animal Feed Science and Technology 40, 109-119.
Chanthakhoun, V., Wanapat, M., 2012. The in vitro gas production and ruminal fermentation of various feeds using rumen liquor from swamp buffalo and cattle. Asian Journal of Animal and Veterinary Advances 7, 54-60.
Chen, L.J., Cui, L.Y., Xing, L., Han, L.J., 2008. Prediction of the nutrient content in dairy manure using artificial neural network modeling. Journal of Dairy Science 91, 4822-4829.
Coblentz, W.K., Nellis , S.E., Hoffman, P. C.,  Hall, M.B.,  Weimer, P.J. Esser, N.M., Bertram, M.G., 2013. Unique interrelationships between fiber composition, water-soluble carbohydrates, and in vitro gas production for fall-grown oat forages. Journal of Dairy Science 96, 7195-7209.
Craninx, M., Fievez, Vlaeminck, V.B., De Baets, B., 2008. Artificial neural network models of the rumen fermentation pattern in dairy cattle. Computers and Electronics in Agriculture  60, 226-238.
Delavar, M.H., Tahmasbi, A.M., Danesh-Mesgaran, M., Valizadeh, R., 2014. In vitro rumen fermentation and gas production: influence of different by-product feedstuffs.  Annual Research & Review in Biology 4, 1121-1128.
Dong, R.L., Zhao, G.Y., 2013. Relationship between the methane production and the CNCPS carbohydrate fractions of rations with various concentrate/roughage ratios evaluated using in vitro incubation technique. Asian-Australasian Journal of Animal Sciences 26, 1708-1716.
Elghandour, M.M.Y., Salem, A.Z.M., Gonzalez-Ronquillo, M., Bórquez, J.L.,   Gado, H.M., Odongo, N.E., Peñuelas, C.G., 2013. Effects of exogenous enzymes on in vitro gas production kinetics and ruminal fermentation of four fibrous feeds. Animal Feed Science and Technology 179, 46-53.
Fadare, D.A., Babayemi, O.J., 2007. Modelling the association between in vitro gas production and chemical composition of some lesser known tropical browse forages using artificial neural network. African Journal of Biotechnology 6, 2184-2192.
Ganesan, R., Dhanavanthan, P., Kiruthika, C., Kumarasamy, P., Balasubramanyam, D., 2014. Comparative study of linear mixed-effects and artificial neural network models for longitudinal unbalanced growth data of madras red sheep. Veterinary World 7, 52-58.
García-Rodríguez, J., Ranilla, M.J., France, J., Alaiz-Moretón, H., Carro, M.D., Lopez, S., 2019. Chemical composition, In Vitro digestibility and rumen fermentation kinetics of agro-industrial by-products. Animals 9, 861; 10.3390/ani9110861.
Getachew, G., Blummel, M., Makkar, H.P.S., Becker, K., 1998. In vitro gas measuring techniques for assessment of nutritional quality of feeds: a review. Animal Feed Science Technology 72, 261-281.
Giridhar, K.S., 2018. Nutritional potentialities of some tree leaves based on polyphenols and rumen in vitro gas production. Veterinary World  23, 1479-1485.
Haddi, M.L., Filacorda, S., Meniai, K. Rollin, F., Susmel, P., 2023. In vitro fermentation kinetics of some halophyte shrubs sampled at three stages of maturity. Animal Feed Science and Technology 104, 215-225.
Hariadi, B.T., Santoso, B., 2010. Evaluation of tropical plants containing tannin on in vitro methanogenesis and fermentation parameters using rumen fluid. The Journal of the Science of Food and Agriculture 90, 456-461.
Haykin, S., 1998. Neural Networks: A Comprehensive Foundation.  Macmillan.
He, L.W., Meng, Q.X., Li, D.Y., Wang, F., Ren, L.P., 2015. Effect of steam explosion on in vitro gas production kinetics and rumen fermentation profiles of three common straws. Italian Journal of Animal Science 14, 4076; 10.4081/ijas.2015
He, Y., Mouthier, T.M.B., Kabel, M. A, Dijkstra, J., Hendriks, W.H., Struik, P. C. and Cone, J.W., 2018. Lignin composition is more important than content for maize stem cell wall degradation. Journal of the Science of  Food and Agriculture 98, 384-390.
Hetta, M., Cone, J.W., Bernes, G., Gustavsson, A-M., Martinsson, K., 2007. Voluntary intake of silages in dairy cows depending on chemical composition and in vitro gas production characteristics. Livestock Science 106, 47-56.
Ipharraguerre, I.R., Clark, J.H., 2003. Soyhulls as an alternative feed for lactating dairy cows: A Review. Journal of Dairy Science 86, 1052-1073.
Kafilzadeh, F., Heidary N., 2013. Chemical composition, in vitro digestibility and kinetics of fermentation of whole-crop forage from 18 different varieties of oat (Avena sativa L.). Journal of Applied Animal Research 41, 1, 61-68.
Kamalak, A., Canbolat, O., Gurbuz, Y., Ozay, O., Ozkan, C.O., Sakarya, M., 2004. Chemical composition and in vitro gas production characteristics of several tannin containing tree leaves. Livestock Research for Rural Development 16 (6). 
Kamalak, A., Canbolat, O., Gurbuz, Y., Ozay, O., 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, 60-67.
Karabulut, A., Canbolat, O. Kalkan, H. Gurbuzoll, F. Sucu E., Filya,  I., 2007. Comparison of In vitro gas production, metabolizable energy, organic matter digestibility and microbial protein production of some legume hays. Asian-Australian Journal of Animal Sciences 20, 517-522.
Kiran, D., Krishnamoorthy, U., 2007. Rumen fermentation and microbial biomass synthesis indices of tropical feedstuffs determined by the in vitro gas production technique. Animal Feed Science and Technology 134, 170-179.
Kulivand, M., Kafilzadeh F., 2015. Correlation between chemical composition, kinetics of fermentation and methane production of eight pasture grasses. Acta Scientiarum: Animal Sciences 37, 9-14.
Lashkari, S., Taghizadeh, A., 2015. Digestion kinetics of carbohydrate fractions of citrus by-products. Veterinary Research Forum 6, 41-48.
Marcos, C.N., García-Rebollar, P, de Blas, C., Carro, M.D., 2019. Variability in the chemical composition and In vitro ruminal fermentation of olive cake by-products. Animal. 9, 109; 10.3390/ani9030109.
Menke, K.H., and Steingass, H., 1988. Estimation of the energetic feed value from chemical analysis and in vitro gas production using rumen fluid. Animal Research and Development 28, 7-55.
Moyo, M. Gueguim Kana E.B. Nsahlai I.V., 2017. Modelling of digesta passage rates in grazing and browsing domestic and wild ruminant herbivores. South African Journal of Animal Science 47, 362-377.
Nherera, F.V., Ndlovu, L.R., Dzowela, B.H., 1999. Relationships between in vitro gas production characteristics, chemical composition and in vivo quality measures in goats fed tree fodder supplements. Small Ruminant Research 31, 117-126.
Njubi. D., Wakhungu, J., Badamana, M., 2010. Use of test-day records to predict first lactation 305-day milk yield using artificial neural network in Kenyan Holstein–Friesian dairy cows. Tropical Animal Health and Production 42, 639-644.
Rodrigues, M.A.M., Fonseca, A.J.M., Sequeira, C.A., Dias-da-Silva, A.A., 2002. Digestion kinetic parameters from an in vitro gas production method as predictors of voluntary intake of forage by mature ewes. Animal Feed Science and Technology 95, 133-142.
Sallam, S.M.A., Nasser, M.E.A., El-Waziry, A.M., Bueno, I.C.S., Abdalla, A.L., 2007. Use of an in vitro rumen gas production technique to evaluate some ruminant feedstuffs. The Journal of Applied Sciences Research 3, 34-41.
Sanzogni, L., Kerr, D., 2001. Milk production estimates using feed forward artificial neural networks. Computers and Electronics in Agriculture 32, 21-30.
SAS, 2011. SAS User’s Guide: Statistics. SAS Institute Inc., Cary, North Carolina. USA.
Tikam, K., Phatsara, C., Sorachakula, C., Vearasilp, T., Samiprem, S., Cherdthong, A., Gerlach, K., Südekum, K.H., 2015. In vitro gas production, in vivo nutrient digestibilities, and metabolisable energy concentrations for sheep of fresh and conserved pangola grass. Small Ruminant Research 128, 34-40.
Van Soest, P.J., Robertson, J.B., Lewis, B.A., 1991. Methods for dietary fiber, neutral detergent fiber and non-starch polysaccharides in ration to animal nutrition. Journal of Dairy Science 74, 3583-3597.
Journal of Livestock Science and Technologies

Articles in Press, Accepted Manuscript
Available Online from 01 February 2026

Files

Share

How to cite

Statistics