Multivariate modeling strategies to predict nutritional requirements of essential amino acids in semiheavy second-cycle hens

• Autor(a) principal: Morales-Suárez,Walter
• Data de Publicação: 2021
• Outros Autores: Ospina-Rojas,Iván Camilo, Méndez-Arteaga,Jonh Jairo, Ferreira,Adriana Helena do Nascimento, Váquiro-Herrera,Henry Alexander
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Brasileira de Zootecnia (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982021000100608
• topic: bird nutrition; egg laying; mathematical model; multivariate analysis; nonlinear model; poultry

ABSTRACT An experiment with 23 diets was performed to evaluate the effect of digestible lysine (Lys), digestible methionine + cysteine (Met+Cys), and digestible threonine (Thr) on egg production of H&N Brown second-cycle laying hens (SCLH) for 20 weeks (92-111 weeks of age) in cages under environmental conditions. Body weight (BW), feed intake (FI), feed conversion ratio (FCR), egg weight (EW), number of hen-housed eggs, and livability were also evaluated during the experiment. Diets were formulated from a central composite design that combined five levels of Lys, Met+Cys, and Thr ranging from 727 to 1159, 662 to 1055, and 552 to 882 mg/kg, respectively. Egg production (EP) data were evaluated through three different modeling strategies: egg production models, multivariate polynomial models, and artificial neural networks (ANN). A cascade-forward neural network with log-sigmoid transfer function was selected as the best model according to goodness-of-fit statistics in both identification and validation data. One of the best scenarios for EP of H&N Brown SCLH under specific outdoor conditions was established at Lys, Met+Cys, and Thr levels of 1138, 1031, and 717 mg/hen·day, respectively. The ANN model may be an appropriate tool to study and predict EP of H&N Brown SCLH based on the combination of three different levels of essential digestible amino acids. The strategies included in this work may contribute to improving poultry performance based on modeling techniques to study other production parameters in terms of different nutritional requirements and productive conditions.