Abstract

GALEANO-VASCO, Luis; CERON-MUNOZ, Mario; RODRIGUEZ, Daniel  and  COTES, José M. Using the distributed-delay model to predict egg production in laying hens. Rev Colom Cienc Pecua [online]. 2013, vol.26, n.4, pp.270-279. ISSN 0120-0690.

Background: using mathematical models to characterize and estimate egg production curves is of great importance for assessing the productive efficiency of hens. These models can be used in identifying and modeling real-time factors affecting animal production and implementing corrective measures to minimize its effect. Objective: we compared the ability to model and adjust the egg production curve in hens using the distributed-Delay model versus the Adams-Bell and Lokhorst models. Methods: 225 records of weekly production of Hy Line Brown (62 data), Lohmann LSL (54 data), Isa Brown (54 data), and Lohmann Brown (55 data) were used. All analyzed flocks were raised at Hacienda La Montaña Farm, owned and managed by the University of Antioquia (Colombia). Models used were Adams-Bell, Lokhorst and Delay; all were validated and contrasted by Durbin-Watson statistic, MAD, determination (R²) and correlation (r) coefficients. Results: the Delay and Lokhorst models resulted in R² values greater than 0.8 and r-values greater than 0.9 (p<0.01). For the Lohmann Brown curve, the Adams-Bell model had the lowest R2 value (0.81), while the Lokhorst and Delay models resulted in the highest R² value for the Isa Brown curve (1.0). The Delay model fit the curve (28 and 40 for the k parameter; 63 and 64 for the DEL parameter). The Hy Line Brown curve presented a high number of irregularities, generating great difficulty for adjustment with the evaluated models. Conclusion: Delay and Lokhorst models are efficient for predicting egg production curve of the bird strains tested. Unlike the Adams-Bell and Lokhorst models, goodness of fit of the Delay model could be increased by including physiological relationships and supply/demand of resources as input variables, which would allow the model to fit the fluctuations observed in the production curves.

Keywords : mathematical model; modeling; regression analysis.

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