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

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.

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.

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.

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.