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 [...]
Highlights
Molting is a natural period of metabolic and physiological changes in birds with seasonal reproduction, but can be induced in poultry to rejuvenate laying hens for a second or third production cycle, resulting in higher egg production (EP), egg weight (EW), and egg quality parameters (Andreatti Filho et al, 2019; Wolc et al, 2020)
Feed intake was similar among diets, between 114.3 and 116.7 g/hen·day, which was lower than that reported by the Hy-Line Brown management guide (Hy-Line, 2018) for second-cycle laying hens (SCLH) during 20 weeks of production, which registered 106.1 g/hen·day
Concerning production parameters of hen-housed eggs, feed conversion ratio (FCR) and Body weight (BW) evaluated for week 20, the results showed the importance of each essential amino acids (EAA)
Summary
Molting is a natural period of metabolic and physiological changes in birds with seasonal reproduction, but can be induced in poultry to rejuvenate laying hens for a second or third production cycle, resulting in higher egg production (EP), egg weight (EW), and egg quality parameters (Andreatti Filho et al, 2019; Wolc et al, 2020). 2. The dose-response methodology is the most widely used methodology to establish the optimal levels of amino acids in experimental trials, in which a single amino acid is evaluated at a time, and the possible complex interactions between amino acids are overlooked. The dose-response methodology is the most widely used methodology to establish the optimal levels of amino acids in experimental trials, in which a single amino acid is evaluated at a time, and the possible complex interactions between amino acids are overlooked The limitations of this conventional “one factor at a time” approach can be overcome by the use of multivariate modeling methods such as those based on machine learning (Mehri, 2014; Faridi et al, 2016)
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