Due to collinearity among the independent varlates, intake prediction models based on least squares multiple regression are likely to predict poorly with independent data. In addition, the regression coefficients are sensitive to small changes in the estimation data and tend not to reflect causal relationships expected from the results of controlled experimentation. Ridge regression (Hoerl and Kennard, 1970) allows the estimation of new coefficients for the independent variables which overcome these effects of collinearity. In order to assess the usefulness of the method for Intake prediction, ordinary least squares (OLS) models, obtained using backward elimination of variables, and ridge regression models were constructed from the same data and then tested with independent data.Estimation data consisted of results of experiments of IGAP, Hurley and Greenmount College of Agriculture in which growing cattle were individually fed grass silage ad-libitum with or without supplementary feeds. Two subsets of the estimation data were used. Subset A included 395 animals and 36 silages; subset B included 192 animals and 16 silages and was for Hurley data only.
HESS Opinions On the use of laboratory experimentation: "Hydrologists, bring out shovels and garden hoses and hit the dirt"
