303. Beyond Growth: AI-based Adaptive Protein Concept for Comprehensive Animal Development.

Abstract The Ideal Protein Concept has been central to amino acid nutrition by defining dietary amino acid profiles relative to lysine, primarily to support growth and protein deposition. However, modern animal production requires nutritional models that account for multiple biological outcomes, including growth performance, carcass composition, immune function, reproductive development, nutrient efficiency, welfare, and adaptation to environmental or management stressors. The Adaptive Protein Concept proposes an AI-driven evolution of the Ideal Protein Concept by using machine learning to identify context-dependent amino acid patterns associated with specific animal responses. This presentation introduces the Adaptive Protein Concept as a framework for moving from fixed amino acid ratios toward dynamic, trait-specific nutrient inference. Instead of asking whether a diet meets a predefined amino acid profile, the approach asks which amino acids, nutrient interactions, and dietary contexts are most strongly associated with the outcome of interest. Machine learning methods such as gradient boosting machines, random forests, feature-importance ranking, partial dependence plots, and multi-trait heatmaps can be used to detect nonlinear relationships between nutrient supply and biological responses. These tools allow amino acids to be evaluated not only as substrates for protein deposition, but also as potential drivers of broader physiological processes related to development, resilience, and efficiency. The concept also supports a shift in interpretation from single-response requirement estimation to multi-trait decision-making. For example, the amino acid pattern that optimizes average daily gain may not be the same pattern that optimizes carcass leanness, immune competence, reproductive development, or nitrogen utilization. By integrating machine learning with animal nutrition knowledge, the Adaptive Protein Concept provides a pathway to identify amino acid profiles that are specific to production goals, animal status, genotype, diet composition, and environmental conditions. Overall, this presentation will discuss how AI-based nutrient modeling can help animal scientists move beyond growth-centered nutrition toward comprehensive animal development and precision feeding strategies.