Artificial neural network models of the rumen fermentation pattern in dairy cattle

Computers and Electronics in Agriculture

Volumn 60, Issue 2, 2008, Pages 226-238

  Craninx, M ^a   Fievez, V ^a   Vlaeminck, B ^a   De Baets, B ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

Artificial neural networks; Milk fatty acids; Prediction; Rumen fermentation; Volatile fatty acids

Indexed keywords

FATTY ACIDS; FERMENTATION; LEARNING SYSTEMS; LINEAR REGRESSION; NEURAL NETWORKS;

MILK FATTY ACIDS; RUMEN FERMENTATION; VOLATILE FATTY ACIDS;

DAIRY PRODUCTS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CATTLE; CORRELATION; DAIRY FARMING; DATA SET; FATTY ACID; FERMENTATION; MILK; PRINCIPAL COMPONENT ANALYSIS;

BOS;

EID: 38149064358     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2007.08.005     Document Type: Article

References (46)

1. AFRC, 1998. Response in the yield of milk constituents to the intake of nutrients by dairy cows. AFRC Technical Committee on Responses to Nutrients, Report No. 11. CAB International, Wallingford.
2. Baldwin R.L., Thornley J.H.M., and Beever D.E. Metabolism of the lactating cow. II. Digestive elements of a mechanistic model. J. Dairy Sci. 54 (1987) 107-131
3. Bannink A., De Visser H., Klop A., Dijkstra J., and France J. Causes of inaccurate prediction of volatile fatty acids by simulation models of rumen functions in lactating cows. J. Theor. Biol. 198 (1997) 353-366
4. Basheer I.A., and Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43 (2000) 3-31
5. Bauman D.E., and Griinari J.M. Regulation and nutritional manipulation of milk fat: low-fat milk syndrome. Livest. Prod. Sci. 70 (2001) 15-29
6. Bibby J., and Toutenberg H. Prediction and Improvement Estimation in Linear Models (1977), John Willey & Sons, London, UK
7. Brown V.E., Rymer C., Agnew R.E., and Givens D.I. Relationship between in vitro gas production profiles and in vivo rumen fermentation patterns in beef steers fed those forages. Anim. Feed Sci. Technol. 98 (2002) 13-24
8. Bruinenberg M.H., Valk H., Struik P.C., and van Vuuren A.M. Fermentation and degradation in the rumen of dairy cows fed on diets consisting of silage from an intensively managed sward and silages from semi-natural grasslands. J. Agric. Sci. 142 (2004) 79-88
9. Cabrita A.R.J., Fonseca A.J.M., Dewhurst R.J., and Gomes E. Nitrogen supplementation of corn silages. 2. Assessing rumen function using fatty acid profiles of bovine milk. J. Dairy Sci. 86 (2003) 4020-4032
10. De Brabander, D.L., Geerts, N.E., De Campeneere, S., Vanacker, J.M., 2004. Effect of starch content of maize silage based diets for dairy cattle. In: Ynze van der Honing (Ed.), Book of Abstracts of the 55th Annual Meeting of the European Association for Animal Production, 5-9 September 2004, Bled, Slovenia, p. 97.
11. De Brabander, D.L., De Campeneere, S., Vanacker, J.M., Geerts, N.E., 2005. Effect of rumen escape starch in maize silage based diets for dairy cattle. In: Ynze van der Honing (Ed.), Book of Abstracts of the 56th Annual Meeting of the European Association for Animal Production, 5-8 June 2005, Uppsala, Sweden, p. 134.
12. ® (2005), The MathWorks Inc., Natick
13. Dewhurst R.J., Fisher W.J., Tweed J.K.S., and Wilkins R.J. Comparison of grass and legume silages for milk production. 1. Production responses with different levels of concentrate. J. Dairy Sci. 86 (2003) 2598-2611
14. Dijkstra J., Neal H.D., Beever D.E., and France J. Simulation of nutrient digestion, absorption and outflow in the rumen: Model Description. J. Nutr. 122 (1992) 2239-2259
15. Fievez V., Vlaeminck B., Dhanoa M.S., and Dewhurst R.J. Use of principal component analysis to investigate the origin of heptadecenoic and conjugated linoleic acids in milk. J. Dairy Sci. 86 (2003) 4047-4053
16. Friggens N.C., Oldham J.D., Dewhurst R.J., and Horgan G. Proportions of volatile fatty acids in relation to the chemical compositions of feeds based on grass silage. J. Dairy Sci. 81 (1998) 1331-1344
17. Heald C.W., Kim T., Sischo W.M., Cooper J.B., and Wolfgang D.R. A computerized mastitis decision aid using farm-based records: an artificial neural network approach. J. Dairy Sci. 83 (2000) 711-720
18. Hindle V.A., van Vuuren A.M., Klop A., and Cone J.W. An evaluation of in vitro estimates for the amounts of rumen by-pass starch and whole tract starch digestibility in dairy cattle. J. Anim. Physiol. Anim. Nutr. 89 (2005) 158-165
19. Kalscheur K.F., Tetet B.B., Piperova L.S., and Erdman R.A. Effect of dietary forage concentration and buffer addition on duodenal flow of trans-C-18:1 fatty acids and milk fat production in dairy cows. J. Dairy Sci. 80 (1997) 2104-2114
20. Lek S., and Guégan J.F. Artificial neural networks as a tool in ecological modelling, an introduction. Ecol. Model. 120 (1999) 65-73
21. Loor J.J., Ferlay A., Ollier A., Doreau M., and Chilliard Y. Relationship among trans and conjugated fatty acids and bovine milk fat yield due to dietary concentrate and linseed oil. J. Dairy Sci. 88 (2005) 726-740
22. Maier H.R., and Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Model. Softw. 15 (2000) 101-124
23. Mc Donald P., Edward R.A., Greenhalgh J.F.D., and Morgan C.A. Animal Nutrition. 5th ed. (1995), John & Sons, New York
24. Møller M. A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6 (1993) 525-533
25. Moorby J.M., Dewhurst R.J., Evans R.T., and Danelón J. Effects of dairy cow diet forage proportion on duodenal nutrient supply and urinary purine derivative excretion. J. Dairy Sci. 89 (2006) 3552-3562
26. Olden J.D., and Jackson D.A. Illuminating the "black box": a randomization approach for understanding variable contributions in artificial neural networks. Ecol. Model. 154 (2002) 135-150
27. Olden J.D., Joy M.K., and Death R.G. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol. Model. 178 (2004) 289-397
28. Özesmi S.L., Tan C.O., and Özesmi U. Methodological issues in building, training and testing artificial neural networks in ecological applications. Ecol. Model. 195 (2006) 83-93
29. Pietersma D., Lacroix R., Lefebvre D., and Wade K.M. Performance analysis for machine-learning experiments using small datasets. Comput. Electron. Agric. 38 (2003) 1-17
30. Piramuthu S. Evaluating feature selection methods for learning in data mining applications. Eur. J. Oper. Res. 156 (2004) 483-494
31. Plumb A.P., Rowe R.C., York P., and Brown M. Optimisation of the predictive ability of artificial neural network (ANN) models: a comparison of three ANN programs and four classes of training algorithms. Eur. J. Pharm. Sci. 25 (2005) 395-405
32. Rymer C., and Givens D.I. Relationships between patterns of rumen fermentation measured in sheep and in situ degradability and the in vitro gas production profile of the diet. Anim. Feed Sci. Technol. 101 (2002) 31-44
33. Salehi F., Lacroix R., and Wade K.M. Improving dairy yield predictions through combined record classifiers and specialized artificial neural networks. Comput. Electron. Agric. 20 (1998) 199-213
34. Shingfield K., Ahvenjärvi S., Toivonen V., Ärölä A., Nurmela K.V.V., Huhtanen P., and Griinari J. Effect of dietary fish oil on biohydrogenation of fatty acids and milk fatty acid content in cows. Anim. Sci. 77 (2003) 165-179
35. Shingfield K.J., Salo-Väänänen P., Pahkala E., Toivonen V., Jaakhola S., Piironen V., and Huhtanen P. Effect of forage conservation method, concentrate level and propylene glycol on the fatty acid composition and vitamin content of cows' milk. J. Dairy Res. 72 (2005) 1-13
36. Skapura D. Building Neural Networks (1996), Addison-Wesley, New York
37. Stefanon B., Volpe V., Moscardini S., and Gruber L. Using artificial neural networks to model the urinary excretion of total and purine derivative nitrogen fractions in cows. J. Nutr. 131 (2001) 3307-3315
38. St-Pierre N.R. Reassessment of biases in predicted nitrogen flows to the duodenum by NRC 2001. J. Dairy Sci. 86 (2003) 344-350
39. Sutton J.D. Digestion and absorption of energy substrates in the lactating cow. J. Dairy Sci. 68 (1985) 3376-3393
40. Vlaeminck B., Fievez V., Van Laar H., and Demeyer D. Rumen odd and branched chain fatty acids in relation to in vitro volatile fatty acid productions and dietary characteristics of incubated substrates. J. Anim. Physiol. Anim. Nutr. 88 (2004) 401-411
41. Vlaeminck B., Dufour C., van Vuuren A.M., Cabrita A.R.J., Dewhurst R.J., Demeyer D., and Fievez V. Potential of odd and branched chain fatty acids as microbial markers: evaluation in rumen contents and milk. J. Dairy Sci. 88 (2005) 1031-1041
42. Vlaeminck B., Fievez V., Tamminga S., Dewhurst R.J., van Vuuren A., De Brabander D., and Demeyer D. Milk odd and branched-chain fatty acids in relation to the rumen fermentation pattern. J. Dairy Sci. 89 (2006) 3954-3964
43. Vlaeminck B., Fievez V., Cabrita A.R.J., Fonseca A.J.M., and Dewhurst R.J. Factors affecting odd and branched chain fatty acids in milk: a review. Anim. Feed Sci. Technol. 131 (2006) 389-417
44. Vlaeminck B., Fievez V., Demeyer D., and Dewhurst J. Effect of forage:concentrate ratio on fatty acid composition of rumen bacteria isolated from ruminal and duodenal digesta. J. Dairy Sci. 89 (2006) 2668-2678
45. Walczak S., and Cerpa N. Heuristic principles for the design of artificial neural networks. Inf. Softw. Technol. 41 (1999) 107-117
46. Weimer P.J., Waghorn G.C., Odt C.L., and Mertens D.R. Effect of diet on population of three species of ruminal cellulolytic bacteria in lactating dairy cows. J. Dairy Sci. 82 (1999) 122-124