Use of artificial neural network to model reproductive performance and mortality of non-descript rabbits

• Autor(a) principal: Yakubu, Abdulmojeed
• Data de Publicação: 2020
• Outros Autores: Nimyak, Philip
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Acta Scientiarum. Animal Sciences (Online)
• Texto Completo: https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/47715
• topic: connectionist; reproduction; lagomorphs; tropics.; connectionist; reproduction; lagomorphs; tropics.

This study was carried out to predict average number of kits per birth and mortality number of non-descript rabbits in Plateau State, Nigeria using artificial neural network (ANN). Data were obtained from a total of 100 rabbit farmers. The predicted mean value for number of kits per birth using ANN (6.60) was similar to the observed value (6.52). As regards mortality, the predicted mean value using ANN (17.75) was also similar to the observed value (17.80). Primary occupation, experience in rabbit keeping, flock size and credit type were the parameters of utmost importance in predicting number of kits per birth. The fairly high coefficient of determination (R2) (55.7%) and low root mean square error (RMSE) value of 1.22 conferred reliability on the ANN model. The R2 value obtained in the prediction of mortality using ANN implies that 61.1% of the variation in the number of mortality can be largely explained by the explanatory variables such as flock size, age of farmers, experience in rabbit keeping and average number of kits per birth. The low RMSE value of 3.82 also gave credence to the regression model. The present information may be exploited in taking appropriate management decisions to boost production.