Establishment of an Amino Acid Nutrition Prediction Model for Laying Hens During the Brooding and Early-Growing Period

TL;DR

This study creates a dynamic model to predict amino acid needs in young laying hens, improving feeding efficiency and sustainability.

Contribution

The study introduces a novel dynamic factorial model for amino acid requirements in Hy-Line Gray laying hens during early growth.

Findings

The model predicts amino acid requirements for 18 amino acids across developmental stages with ranges like aspartic acid (0.1–0.863 g/day) and glutamic acid (0.170–1.503 g/day).

The model demonstrated strong predictive validity over a 12-week growth period.

The factorial framework allows precise adjustment of amino acid provisions to match physiological needs.

Abstract

This study advances the field of poultry nutrition by introducing a dynamic modeling approach to estimate amino acid requirements for layer chicks, addressing limitations in traditional static models. This approach aligns with recent trends advocating for precision nutrition in poultry farming, where tailored feeding strategies optimize both growth efficiency and cost-effectiveness. Positioned within the literature, this research enhances the toolkit for precision livestock nutrition, offering a scalable framework applicable to other poultry species or phases. Its emphasis on adaptability and biological relevance addresses critical challenges identified in earlier studies, such as the oversimplification of nutrient requirements in heterogeneous populations.

The aim of this study was to develop a dynamic factorial model for predicting amino acid requirements in Hy-Line Gray laying hens during critical early growth stages (0–84 days), addressing the need for precision feeding in modern poultry production systems. Methods: Four sequential trials were conducted. In Trial 1, growth curves and protein deposition equations were developed based on fortnightly body composition analyses, with parameters evaluated using the Akaike and Bayesian information criteria (AIC and BIC). In Trial 2, the carcass and feather amino acid profiles were characterized via HPLC. And established the amino acid composition patterns of chicken feather protein and carcass protein (AAF and AAC). In Trial 3, maintenance requirements were quantified through nitrogen balance studies, and in Trial 4, amino acid patterns of feather protein (APD) and apparent protein digestibility (ADD) were established using an endogenous indicator method. These datasets were integrated through factorial modeling to predict age-specific nutrient demands. Results: The developed model revealed the following quantitative requirements (g/day) for 18 amino acids across developmental stages: aspartic acid (0.1–0.863), glutamic acid (0.170–1.503), serine (0.143–0.806), arginine (0.165–0.891), glycine (0.258–1.279), threonine (0.095–0.507), proline (0.253–1.207), alanine (0.131–0.718), valine (0.144–0.737), methionine (0.023–0.124), cysteine (0.102–0.682), isoleucine (0.086–0.458), leucine (0.209–1.067), phenylalanine (0.086–0.464), histidine (0.024–0.133), lysine (0.080–0.462), tyrosine (0.050–0.283), and tryptophan (0.011–0.060). The model demonstrated strong predictive validity throughout the 12-week growth period. Conclusion: This integrative approach yielded the first dynamic requirement model for Hy-Line Gray layers during early development. The factorial framework enables precise adjustment of amino acid provisions to match changing physiological needs and has high potential value in optimizing feed efficiency and supporting sustainable layer production practices.

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