Article

Response of male BUT big 6 turkeys to varying amino acid feeding programs.

Degussa AG, FA-M-AN, Rodenbacher Chaussee 4, 63457 Hanau, Germany.
Poultry Science (Impact Factor: 1.54). 05/2006; 85(4):652-60. DOI: 10.1093/ps/85.4.652
Source: PubMed

ABSTRACT Dietary protein is a major cost contributor in turkey nutrition. Therefore, a feeding trial with male BUT Big 6 turkeys to 154 d of age was conducted to examine how live performance and economics are affected when dietary amino acid levels are altered in different phases. Six dietary treatments were run with treatment 1 as the control in which balanced protein levels were according to recommendations during all 6 phases. Treatments 2 through 6 used combinations of balanced protein (based on lysine) that ranged from 90 to 120% of those used in treatment 1. The combinations for the 6 phases of feeding were 120, 120, 120, 120, 90, and 90% for treatment 2; 120, 120, 120, 100, 90, and 90% for treatment 3; 120, 120, 100, 100, 90, and 90% for treatment 4; 120, 120, 120, 120, 100, and 100% for treatment 5; and 90, 90, 90, 100, 100, and 100 for treatment 6. Final BW was highest in treatment 4 and lowest in treatment 2 (P < 0.05), whereas final BW were intermediate and statistically not different in treatments 1, 3, 5, and 6. Breast meat yield was highest in treatment 5 and lowest in treatments 2 and 3 (P < 0.05). Mortality seemed to be reduced in treatment 6 compared with treatments 2, 3, and 5 (P < 0.10). Performance data in combination with economic simulations suggested that the feeding regimens of treatments 4 or 6 might be alternative strategies to treatment 1 to improve overall profitability.

0 Bookmarks
 · 
127 Views
  • Source
    01/2009;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Three Narushin-Takma (NT) models (NT1, NT2, and NT3) were examined for their ability to describe different curves obtained from broiler breeder flocks. The models NT1, NT2, and NT3 comprise 3 flexible mathematical functions (rational polynomial functions) with 5, 6, and 7 parameters, respectively. The characteristics fitted were BW, egg production, egg mass, egg weight, first- and second-grade eggs, hatchability, feed intake, and feed conversion ratio. To evaluate the ability of these NT models to fit the different curves, comparisons were made with more commonly fitted functions (Gompertz, modified compartmental, Richards, Adams-Bell, and Lokhorst). Comparisons revealed a higher accuracy of fit with the NT models, proving their general flexibility. This study likely represents the first time a generic model has been demonstrated to fit all these characteristics satisfactorily. Results showed that in most cases, NT3, because of its greater number of parameters, gave the highest accuracy of prediction. The NT models are likely to fit most curves and are therefore advocated for accurate prediction of other traits with a minimum of mathematical complexity.
    Poultry Science 02/2011; 90(2):507-15. · 1.54 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Neural networks (NN) are a relatively new option to model growth in animal production systems. One self-organizing submodel of artificial NN is the group method of data handling (GMDH)-type NN. The use of such self-organizing networks has led to successful application of the GMDH algorithm over a broad range of areas in engineering, science, and economics. The present study aimed to apply the GMDH-type NN to predict caloric efficiency (CE, g of gain/kcal of caloric intake) and feed efficiency (FE, kg of gain/kg of feed intake) in tom and hen turkeys fed diets containing different energy and amino acid levels. Involved effective input parameters in prediction of CE and FE were age, dietary ME, CP, Met, and Lys. Quantitative examination of the goodness of fit for the predictive models was made using R2 and error measurement indices commonly used to evaluate forecasting models. Statistical performance of the developed GMDH-type NN models revealed close agreement between observed and predicted values of CE and FE. In conclusion, using such powerful models can enhance our ability to predict economic traits, make precise prediction of nutrition requirements, and achieve optimal performance in poultry production.
    Poultry Science 06/2010; 89(6):1325-31. · 1.54 Impact Factor

Full-text

Download
2 Downloads
Available from