ArticlePoultry science2026
Phase-dependent nutrient requirements of broiler breeder hens for day-old chick production in a field study using interpretable machine learning.
Article in Poultry science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Day-old chick output from broiler breeder flocks is a key determinant of efficiency in the global poultry industry and is highly sensitive to breeder nutrition. This study aimed to quantify phase-dependent requirements of metabolizable energy and digestible amino acids (lysine, methionine, and threonine) using a large-scale commercial dataset and an interpretable modeling framework. Data from 2,685 flock-week observations (2016-2022) were analyzed across three production phases: early (26-32 weeks), peak (33-50 weeks), and late (51-62 weeks). Nutrient-response relationships were modeled using both classical regression and a single-neuron artificial neural network with a hyperbolic tangent activation, enabling flexible, nonlinear dose-response estimation while preserving biological interpretability. Nutrient requirements were defined as the intake required to achieve 95% of the asymptotic response and were estimated with bootstrap-derived confidence intervals. The ME requirements were highest in early production (453 kcal/d) and declined slightly during peak (449 kcal/d) and late production (448 kcal/d); the narrow numerical range across phases warrants cautious interpretation despite statistical significance. In contrast, amino acid requirements followed reproductive intensity, peaking during mid-production (digestible lysine 1.03 g/d, methionine 0.68 g/d, threonine 0.80 g/d) and decreasing thereafter. The nonlinear modeling approach consistently captured saturating responses that were not adequately described by linear models. These results demonstrate that nutrient requirements in broiler breeders are phase-dependent and can be effectively quantified using interpretable machine learning applied to field data. The proposed framework provides a practical tool for deriving biologically meaningful requirement estimates and supports more precise, phase-specific feeding strategies in commercial production systems.
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