Prediction of dietary digestible phosphorus in broilers diets: Insights from a meta-analysis

Optimizing dietary phosphorus (P) and calcium (Ca) levels in broiler is essential for improving nutrient utilization and minimizing environmental impact. This study aimed to develop a predicative equation for apparent precaecal digestible P (PCdigP) in broiler diets based on a meta-analysis using 343 treatments from 48 publications, in relation to dietary Ca, phytate-phosphorus (PP), non-phytate phosphorus (NPP), and microbial phytase (MPhy). Nutrient values were standardized using feed composition tables to ensure consistency across datasets. A generalized linear mixed model was applied, considering the experiment as a random effect. Results indicated that NPP was the most significant predictor of PCdigP, while increasing dietary Ca had a negative effect (P = 0.007). Increasing phytate-P increased PCdigP even without phytase, indicating that part of the P can be used by birds. When MPhy is supplemented, an interaction showed that increasing PP increased digestibility but this effect is not linear (interaction PP x MPhy x MPhy), which highlights the importance of phytase in high-PP diets. A three-way interaction among Ca, NPP, and MPhyt (P = 0.04) confirmed the complex nutrient interactions influencing PCdigP. The developed model was then validated through an in vivo experiment with 576 one-day Ross broilers subjected to eight dietary treatments with varying PCdigP levels. Apparent prececal digestible P measurements were compared to predicted values, demonstrating moderately high accuracy (R² = 55.6%). Including experimentally determined PP disappearance in the model improved the accuracy to 82.7%, highlighting the model's limitations in fully capturing animals adaptive responses to extreme diets. Evaluation metrics confirmed the robustness of the model (RMSPE = 16,0%), which outperformed alternative models based on available phosphorus (aP) and NPP-based predictions. In conclusion, the proposed PCdigP model is a reliable tool for broiler diet formulation by integrating key dietary variables and their interactions, enabling robust predictions based on standard chemical analysis of the complete diet. However, caution is needed when applying the model to diets with extreme NPP and Ca levels, as physiological adaptations may affect digestibility responses not captured by the model.