Optimal in-feed amino acid ratio for broiler breeder hens based on deletion studies

An ideal amino acid ratio (IAAR) for breeder hens is needed for maximum nitrogen retention (NR) taking into account nitrogen deposition in body (ND_B ), feathers (ND_F ) and egg mass (NEM) to improve dietary protein efficiency. Thus, the aim of this study was to apply the deletion method to derive the IAAR for broiler breeder hens. The nitrogen balance trials were performed from 31 to 35 weeks and from 46 to 50 weeks. Twelve treatments with eight replicates and one hen per cage were used. A balanced diet (BD) was formulated to meet the requirement of all nutrients. The other diets were formulated diluting 55% of BD with corn starch and refilled with amino acids (AAs) and other ingredients, except the AA tested. Each trial lasted 25 days. Feather losses, egg production and egg weight were recorded daily, and the samples were stored to further determine NEM and nitrogen in feather losses (ND_FL ). At the start and the end of each period, a group of birds were slaughtered to further determine ND_B and ND_F . The NR was calculated as the sum of ND_B , ND_F , ND_FL , NEM and the nitrogen maintenance requirement (NMR). The deletion of valine greatly depressed the NR in peak production (31 to 35 weeks) while the deletion of the isoleucine greatly depressed the NR of the hens from 46 to 50 weeks of age. The percentual reduction in NR and the per cent of the AA to delete from the BD were used to calculate the AA requirement. The average IAAR was Lys 100, Met+Cys 86, Trp 23, Thr 80, Arg 113, Val 90, Ile 91, Leu 133, Phe+Tyr 108, Gly+Ser 94 and His 35. The IAAR was in line with the recommendation from the literature, validating deletion method with the advantages from a rapid and low-cost procedure.

Description of a model to optimise the feeding of amino acids to growing pullets

1. A nutrition model is described that may be used to optimise the amino acid nutrition of laying-type pullets prior to the onset of lay. It is not a method of optimising lifetime laying performance. 2. The potential growth and composition of the body, feathers, ovary and oviduct are described from hatching to the age at which sexual maturity is attained, from which the daily amino acid and energy requirements for the average individual in the population can be calculated. 3. There are two parts to the approach used, the first being a description of the model itself and the second being a description of how the required information was gathered. A number of assumptions made in developing the model are discussed. 4. The rates of maturing of the body, feather-free body and body protein of the DeKalb pullets used were shown to be constant at 0.017/d and those of feathers at 0.02/d. These are considerably slower than those of the oviduct and ovary (0.139 and 0.084/d respectively). The ovary attained a higher mature weight (78.4 vs. 58.7 g) than the oviduct. 5. The age at which the growth of the reproductive organs is initiated in the model is defined by the user. The daily amount of threonine required to meet the requirements for maintenance and potential growth of the developing tissues and organs is calculated for each day of the growing period from hatching to the age at which the first egg is laid. A method of calculating the required daily concentration of threonine in the feed is described, from which a feeding programme may be derived. 6. For the model to produce an optimum economic feeding programme for a population of laying-type pullets, more information than is available from this study is required. These issues are discussed in the paper.

Fuzzy modeling to predict chicken egg hatchability in commercial hatchery

Experimental studies have shown that hatching rate depends, among other factors, on the main physical characteristics of the eggs. The physical parameters used in our work were egg weight, eggshell thickness, egg sphericity, and yolk per albumen ratio. The relationships of these parameters in the incubation process were modeled by Fuzzy logic. The rules of the Fuzzy modeling were based on the analysis of the physical characteristics of the hatching eggs and the respective hatching rate using a commercial hatchery by applying a trapezoidal membership function into the modeling process. The implementations were performed in software. Aiming to compare the Fuzzy with a statistical modeling, the same data obtained in the commercial hatchery were analyzed using multiple linear regression. The estimated parameters of multiple linear regressions were based on a backward selection procedure. The results showed that the determination coefficient and the mean square error were higher using the Fuzzy method when compared with the statistical modeling. Furthermore, the predicted hatchability rates by Fuzzy Logic agreed with hatching rates obtained in the commercial hatchery.