Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers

Efficient meat production is crucial in addressing global market demands and sustainability goals. Modeling production systems has gained worldwide attention, offering valuable insights for predicting outcomes and optimizing economic returns. In the poultry industry, researchers have developed mathematical models to predict animal performance and maximize profits. These models incorporate theories to explain real-world processes and enable future event predictions. One such model is the Broiler Growth Model (BGM), which serves as a predictive tool for estimating feed intake, growth, and body composition of broilers. The BGM takes into account the genetic potential of the broilers, the feed they are provided, and several constraining factors that may prevent the animal from achieving their genetic potential. To evaluate the BGM, a series of simulations were performed: (i) model behavior was evaluated by simulating the response of males and females from 22 to 35 d to feeds differing in dietary protein content and nutrient density; (ii) model prediction was evaluated using the results of a protein response trial conducted at UNESP in which six dietary protein levels were fed to male and female broilers over a 56 d period; and (iii) model optimization was used to maximize economic returns in the above trial. The model behaved as expected when feeds differing in protein content were fed, with feed intake per kg of BW increasing as protein level was decreased, resulting in lower gains and higher body lipid contents. Increasing nutrient density resulted in higher feed intake in the second level, followed by a reduction in feed intake in the highest nutrient feed. The simulated response to nutrient density resulted in increasing body lipid deposition as the nutrient density increased. In comparing the simulated and actual results of the protein response trial, the overall error of prediction was up to 15% for feed intake, BW, and body protein. The optimization routine allows the simulation of different economic scenarios, helping in decision-making. The Broiler Growth Model emerges as a valuable tool for the poultry industry, offering predictive capabilities and economic optimization potential. While minor discrepancies between simulated and actual results exist, the BGM holds significant promise for enhancing efficiency and profitability in broiler production, contributing to the broader goals of sustainable broiler meat production.