Materi pelatihan

Aplikasi meta-analisis dalam penelitian []

Data for exercise []


St-Pierre, N.R. 2001. Invited review. Integrating quantitative findings from multiple studies using mixed model methodology. Journal of Dairy Science, 84 (4), pp. 741-755. [PDF]

Sauvant, D., Schmidely, P., Daudin, J.J., St-Pierre, N.R. 2008. Meta-analyses of experimental data in animal nutrition. Animal, 2 (8), pp. 1203-1214. [PDF]

Machine learning

Huang, Y., Lan, Y., Thomson, S.J., Fang, A., Hoffmann, W.C., Lacey, R.E. 2010. Development of soft computing and applications in agricultural and biological engineering. Computers and Electronics in Agriculture, 71 (2), pp. 107-127. [PDF]

Huang, Y. 2009. Advances in artificial neural networks – Methodological development and application. Algorithms, 2 (3), pp. 973-1007. [PDF]

Craninx, M., Fievez, V., Vlaeminck, B., De Baets, B. 2008. Artificial neural network models of the rumen fermentation pattern in dairy cattle. Computers and Electronics in Agriculture, 60 (2), pp. 226-238. [PDF]

Mottaghitalab, M., Faridi, A., Darmani-Kuhi, H., France, J., Ahmadi, H. 2010. Predicting caloric and feed efficiency in turkeys using the group method of data handling-type neural networks. Poultry Science, 89 (6), pp. 1325-1331. [PDF]

Ahmadi, H., Mottaghitalab, M., Nariman-Zadeh, N., Golian, A. 2008. Predicting performance of broiler chickens from dietary nutrients using group method of data handling-type neural networks. British Poultry Science, 49 (3), pp. 315-320. [PDF]

System dynamics / Mechanistic modeling

Fernández, C., Espinos, F., López, M.C., García-Diego, F.J., Cervera, C. 2013. Representation of a mathematical model to predict methane output in dairy goats. Computers and Electronics in Agriculture, 91, pp. 1-9. [PDF]

Ramin, M., Huhtanen, P. 2012. Development of an in vitro method for determination of methane production kinetics using a fully automated in vitro gas system-A modelling approach. Animal Feed Science and Technology, 174 (3-4), pp. 190-200. [PDF]