Energy systems and laying hens an evaluation of energy systems for the formulation of practical diets for laying hens

Robust energy systems (ES) are required to execute accurate feed formulation. Based on the premise that the more accurate an ES is, the better it will predict performance, a predictive model was developed to evaluate the veracity of five ES proposed for formulating layer diets. Data were derived from a meta-analysis of three experiments conducted on individually housed brown laying hens flocks. Congruency was demonstrated using a single model to predict feed intake (r = 0.802), justifying their combination into a single, consolidated flock. A matrix of energy values was determined for each ES, and dietary energy content was recalculated for all diets. Hen production parameters were predicted using each ES, and all predictions were similar. The correlation for energy utilisation efficiency for egg output between all ES was high (r > 0.96), suggesting that no one ES offered any advantage. Adjusting dietary energy values according to the lipid, protein or fiber content did not improve predictions. A least-cost feed formulation exercise, using the different ES, showed that the ingredient content of the diets varied considerably, depending on the ES used and the relative cost of ingredients. Differences in feed costs were negligible. The ideal ES system to use for formulation purposes depends on the relative cost of ingredients. In conclusion, the AME n system accurately predicts laying hen production parameters and allows for cost-effective feed formulation of layer diets. There was no advantage in predicting performance or the formulated feed cost for any ES evaluated.

Automated summary

Robust energy systems are essential for accurate feed formulation. A predictive model was developed to evaluate the accuracy of different energy systems for formulating layer diets. The results showed that there was no advantage in predicting performance or the cost of formulated feed for any of the energy systems evaluated.