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Bi-objective optimization of nutrient intake and performance of broiler chickens using Gaussian process regression and genetic algorithm

Authors :
Hamed Ahmadi
Markus Rodehutscord
Wolfgang Siegert
Source :
Frontiers in Animal Science, Vol 4 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

This study investigated whether quantifying the trade-off between the maxima of two response traits increases the accuracy of diet formulation. To achieve this, average daily weight gain (ADG) and gain:feed ratio (G:F) responses of 7–21-day-old broiler chickens to the dietary supply of three nutrients (intake of digestible glycine equivalents, digestible threonine, and total choline) were modeled using a newly developed hybrid machine learning-based method of Gaussian process regression and genetic algorithm. The dataset comprised 90 data lines. Model-fit-criteria indicated a high model adjustment and no prediction bias of the models. The bi-objective optimization scenarios through Pareto front revealed the trade-off between maximized ADG and maximized G:F and provided information on the needed input of the three nutrients that interact with each other to achieve the trade-off scenarios. The trade-off scenarios followed a nonlinear pattern. This indicated that choosing target values intermediate to maximized ADG and G:F after single-objective optimization is less accurate than feed formulation after quantifying the trade-off. In conclusion, knowledge of the trade-off between maximized ADG and maximized G:F and the needed nutrient inputs will help feed formulators to optimize their feed with a more holistic approach.

Details

Language :
English
ISSN :
26736225
Volume :
4
Database :
Directory of Open Access Journals
Journal :
Frontiers in Animal Science
Publication Type :
Academic Journal
Accession number :
edsdoj.6d37ded014884016a8c5888ed2edbd72
Document Type :
article
Full Text :
https://doi.org/10.3389/fanim.2023.1042725