Modelling hatchability and mortality in muscovy ducks using automatic linear modelling and artificial neural network - Yakubu, A.; Dahloum, L.; Shoyombo, A. J.; Yahaya, U. M. (Diponegoro University, 2019)
This study was embarked upon to predict hatchability and mortality rate of Muscovy ducks in Nasarawa State, Nigeria. Data were obtained from a total of 119 duck farmers. The automatic linear modelling (ALM) and artificial neural network (ANN) models were employed. The average flock size was 9.84±0.60 per household. The predicted hatchability mean values using ALM (8.66) and ANN (8.65) were similar to the observed value (8.66). The predicted mortality mean values using ALM (2.95) and ANN (3.03) were also similar to the observed value of 2.95. Experience in duck rearing, the educational status of farmers, source of foundation stock and season were the variables of importance in the prediction of hatchability using ALM and ANN models. However, primary occupation, source of foundation stock, experience in duck rearing, land holding and management system were the important variables automatically selected for the prediction of mortality. Moderate coefficients of determination (R2 = 0.422 vs 0.376) and adjusted R2 (0.417 vs 0.371) estimates were obtained for hatchability and mortality using ALM. Different patterns were obtained under the ANN models as regards the prediction of hatchability (R2= 0.573 and adjusted R2= 0.569) and mortality (R2= 0.615 and adjusted R2= 0.612). The present information may aid management decisions towards better hatchability and mortality performance in Muscovy ducks.