Performance and Egg Quality of Commercial Laying Hens Fed Diets Formulated Using Non-Linear Programming

ABSTRACT Feed formulation using linear programming consists of determining the mixture of feedstuffs required to meet pre-established animal nutritional requirements at the lowest cost. On the other hand, with the use of non-linear programming, it is possible to define nutritional requirements at the time of formulation, aiming at maximum profit. The objective of the present study was to compare feeds formulated using linear and non-linear programming in terms of live performance and internal and external egg quality of commercial laying hens. A total of 288 Hisex® White laying hens, 1.540 ± 0.128 g body weight, were evaluated from 33 to 45 weeks of age. Hens were distributed in a completely randomized block design, including six treatments with six replicates of eight birds each. Three treatments consisted of feeds formulated using linear programming and based on the nutritional requirements of Rostagno et al. (2011), of the genetic strain manual, or mathematical models to maximize performance. The other three treatments consisted of feeds formulated using non-linear programming considering typical, favorable, or unfavorable market scenarios. Data were submitted to analysis of variance, and in case of significance (p<0.05), means were grouped using the Scott-Knott test (5%).The treatments did not influence (p>0.05) Haugh unit, albumen height, or external egg quality parameters. Treatment effects (p<0.05) on yolk weight, albumen weight, yolk color, yolk percentage, albumen percentage, and performance parameters were described. In general, feeds formulated using linear programming and based on nutritional requirements obtained by mathematical models and the genetic strain manual promoted better performance results because the feeds were nutritionally denser. However, the treatments that maximized live performance did not result in higher profitability, which was obtained with the diet formulated for a favorable market scenario using non-linear programming.