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Hameed Akande Bashiru
Saidu Oyarekhua Oseni
Louis Adebayo Omadime


The study assessed the growth of FUNAAB-Alpha chickens (FAC) using spline and non-linear functions in order to establish the most appropriate growth function(s) for FAC. Three hundred (300) day-old chickens of FAC were used for the study. They were raised intensively under a deep litter system for 20 weeks and body weight records were taken weekly with the aid of a digital scale. Spline models of different numbers of, and locations of, knots were fitted using the REG procedure of SAS® while four non-linear models (Gompertz, Logistic, Bertalanffy and Richards') were fitted using the NLIN procedure of SAS®. The estimated hatch weight (β0) for the male and female chickens ranged from 30.77 g to 74.71 g and from 15.56 g to 38.19 g, respectively. The regression coefficients ranged from -38.47 to 47.46 and -39.40 to 40.47 for the male and female, respectively. The highest magnitudes of these coefficients were estimated at early ages (3 to 10 weeks), implying that growth rate at early stage of life might be a key response to selection for later growth performance. For non-linear models, parameter A (or asymptotic weight) for all the models ranged from 3716 g to 2050 g and 1591 g to 3330 g for male and female, respectively. The parameter (B), the scaling parameter (constant of integration), ranged from 0.7541 to 15.441. Likewise, parameter K, which is the maturity index, ranged from 0.0463 to 0.2002. The age at inflection point for FUNAAB-Alpha chickens ranged between 13.30 and 17.63 weeks for male chickens and between 14.23 and 19.94 weeks for female chickens while the corresponding body weight at inflection point ranged between 754 and 1528 g and 586 and 1261 g for male and female chickens, respectively. Based on Akaike Information Criterion and Bayesian Information Criterion as best fit model selection criteria, it was concluded that the spline models of 3 and 4 knots were the best fit linear spline models while Bertalanffy and Gompertz models were selected as the best fit non-linear models.

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