Artificial Neural Network Models of the Ruminal pH in Holstein Steers

Authors: Alireza Vakili, Mohsen Danesh Mesgaran, Majid Abdollazade

Abstract:

In this study four Holstein steers with rumen fistula fed 7 kg of dry matter (DM) of diets differing in concentrate to alfalfa hay ratios as 60:40, 70:30, 80:20, and 90:10 in a 4 × 4 latin square design. The pH of the ruminal fluid was measured before the morning feeding (0.0 h) to 8 h post feeding. In this study, a two-layered feed-forward neural network trained by the Levenberg-Marquardt algorithm was used for modelling of ruminal pH. The input variables of the network were time, concentrate to alfalfa hay ratios (C/F), non fiber carbohydrate (NFC) and neutral detergent fiber (NDF). The output variable was the ruminal pH. The modeling results showed that there was excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 >0.96). Therefore, we suggest using these model-derived biological values to summarize continuously recorded pH data.

Keywords: Ruminal pH, Artificial Neural Network (ANN), Non Fiber Carbohydrate, Neutral Detergent Fiber.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078301

References:

[1] Kennelly, J.J., Robinson, B., Khorasani, G.R., 1999. Influence of carbohydrate source and buffer on rumen fermentation characteristics, milk yield, and milk composition in early-lactation Holstein cows. J. Dairy Sci. 82, 2486-2496.
[2] Hoover, W.H., Miller, T.K., 1995. Optimising carbohydrate fermentation in the rumen. In: Proceedings of the Sixth Annual Florida Ruminant Nutrition Symposium, University of Florida, Gainesville, Florida, pp. 89-95.
[3] AFRC (Agricultural and Food Research Council). 1993. Energy and Protein Requirements of Ruminants. Advisory manual prepared by the Agric. Food Res. Counc. Technical Committee on Responses to Nutrients. CAB International, Wallingford, UK.
[4] NRC, 2001. Nutrient Requirements of Dairy Cattle, 7th ed. National Academy Press, Washington, DC.
[5] Cerrato-Sa'nchez, M., S. Calsamiglia, and A. Ferret. 2007. Effects of Time at Suboptimal pH on Rumen Fermentation in a Dual-Flow Continuous Culture System. J. Dairy Sci. 90:1486-1492.
[6] de Veth, M. J., and E. S. Kolver. 2001a. Diurnal variation in pH reduces digestion and synthesis of microbial protein when pasture is fermented in continuous culture. J. Dairy Sci. 84:2066-2072.
[7] Calsamiglia, S., A. Ferret, and M. Devant. 2002. Effects of pH and pH fluctuations on microbial fermentation and nutrient flow from a dual-flow continuous culture system. J. Dairy Sci. 85:574-579.
[8] Dayhoff, J. E., and J. M. DeLeo. 2001. Artificial neural networks: Opening the black box. ancer 91(Suppl. 8):1615- 1635.
[9] Nelles, O. 2000. Nonlinear system identification from classical approaches to neural networks and fuzzy models. Springer
[10] Bucinski, A., H. Zielinski, and H. Kozlowska. 2004. Artificial neural networks for prediction of antioxidant capacity of cruciferous sprouts. Trends Food Sci. Technol. 15:161-169.
[11] Pitt, R. E., J. S. Van Kessel, D. G. Fox, A. N. Pell, M. C. Barry, and P. J. VanSoest.1996. Prediction of ruminal volatile fatty acids and pH within the net carbohydrate and protein system. J. Anim. Sci. 74: 226-244.
[12] Stone, W. C. 2004. Nutritional Approaches to Minimize Subacute Ruminal Acidosis and Laminitis in Dairy Cattle. J. Dairy Sci. 87:E13- E26.
[13] Mertens, D. R. 1992. Nonstructural and structural carbohydrates. Pages 219 to 235 in Large Dairy Herd Management. H. H. Van Horn and C. J. Wilcox, ed. American Dairy Science Association, Champaign, IL.
[14] Krause, K. M., and D. K. Combs. 2003. Effects of particle size, forage, and grain fermentability on performance and ruminal pH in mid lactation cows. J. Dairy Sci. 86:1382-1397.
[15] Rustomo, B., O. AlZahal, J. P. Cant, M. Z. Fan, T. F. Duffield, N. E. Odongo, and B. W. McBride. 2006a. Acidogenic value of feeds. II. Effects of rumen acid load from feeds on dry mater intake, ruminal pH, fiber degradability, and milk production in the lactating cow. Can. J. Anim. Sci. 86:119-126.
[16] Rustomo, B., O. AlZahal, N. E. Odongo, T. F. Duffield, and B. W. McBride. 2006b. Effects of rumen acid-load from feed and forage particle size on ruminal pH and dry matter intake in the lactating dairy cow. J. Dairy Sci. 89:4758-4768.