Prediction of the effect of enzymes on chick performance when added to cereal-based diets: use of a modified log-linear model

Department of Animal Science, University of Manitoba, Winnipeg, Canada.

A previous study demonstrated that a log equation could be used to predict the relationship between the amount of a crude enzyme added to a diet and chick performance. The objective of the current study was to determine if a modification of the original equation, in conjunction with a computer program, would overcome some of its limitations. The modified equation was Y = A + B log (CX + 1), where Y is the estimated performance value; A is the intercept that represents the performance without enzyme supplementation; B, the slope of the equation (performance change per log unit of an enzyme in the diet), is a measure of an enzyme efficacy; C is an amplified factor; and X is the amount of enzyme in the diet. The results demonstrated that the new model more accurately predicted chick performance than that of the original equation with correlations (r) between chick performance and amount of different enzymes added to the diet ranging from r = 0.80 to 0.99 (P < 0.05). In addition, the same trends were found when the model was used to assess the efficacy of a given enzyme added to corn-, wheat-, barley-, and rye-based diets or for combinations of two dietary components (rye and wheat). The model proposed in this study provides a new means of assessing the overall efficacy of an enzyme preparation. This model could be routinely used by enzyme and livestock producers to establish the best combination of different cereals and enzymes so as to maximize net returns.