Artificial Neural Network Models of the Ruminal pH in Holstein Steers
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.
[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.
[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.
@article{"International Journal of Biological, Life and Agricultural Sciences:61016", author = "Alireza Vakili and Mohsen Danesh Mesgaran and Majid Abdollazade", title = "Artificial Neural Network Models of the Ruminal pH in Holstein Steers", 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.", volume = "4", number = "8", pages = "647-5", }
