Modelling Growth Of Dual-purpose Sasso Hens In The Tropics Using Different Algorithms

This study was embarked upon to evaluate body weight (BW) from age (weeks) of Sasso hens in Nasarawa State, Nigeria. A total of one hundred and eight (108) Sasso hens aged 30 weeks were randomly selected from a larger stock kept at the Livestock Farm. Fifty-four of these birds were kept on deep litter while another fifty-four were reared in battery cages. The birds in each system of management were replicated three times with eighteen birds per replicate in a completely randomized design. In both deep litter and battery cage systems, data were collected on weekly body weights of birds from week 31-52 of rearing. Only data from forty (battery cage) and forty-three (deep litter) surviving birds were eventually used for further analyses. Effect of housing system on BW was subjected to T-Test. Phenotypic correlation between body weight (BW) and age of birds was established in both systems of rearing. Linear, Quadratic, Gompertz, Artificial Neural Network (ANN) and the Classification and Regression Tree (CRT) models were used to predict BW from the age (including housing system for CRT model) of birds. There was no significant (P=0.558) difference in the total average weekly BW of birds on deep litter (3.38 ± 0.12 kg) and those in cages (3.37 ± 0.12 kg). The prediction of BW from age was best fitted using the ANN model in both the deep litter (R2 , adjusted R2 , RMSE and significance level were 87.0%, 87.0%, 0.04 and 0.000) and battery cage (R2 , adjusted R2 , RMSE and significance level were 99.0%, 99.0%, 0.01 and 0.000) systems. The CRT model, however, predicted the optimal BW to be greater than 32.5, but not above 47.5 weeks of age with R2 value of 93.4%. The present findings may be exploited in mapping out appropriate management practices geared towards increased production.