Predicting the Digestible Energy of Rapeseed Meal from Its Chemical Composition in Growing-finishing Pigs
نویسندگان
چکیده
Two experiments were conducted to establish a digestible energy (DE) content prediction model of rapeseed meal for growing-finishing pig based on rapeseed meal's chemical composition. In experiment 1, observed linear relationships between the determined DE content of 22 rapeseed meal calibration samples and proximate nutrients, gross energy (GE) and neutral detergent fiber (NDF) were used to develop the DE prediction model. In experiment 2, 4 samples of rapeseed meal selected at random from the primary rapeseed growing regions of China were used for testing the accuracy of DE prediction models. The results indicated that the DE was negatively correlated with NDF (r = -0.86) and acid detergent fiber (ADF) (r = -0.73) contents, and moderately correlated with gross energy (GE; r = 0.56) content in rapeseed meal calibration samples. In contrast, no significant correlations were found for crude protein, ether extract, crude fiber and ash contents. According to the regression analysis, NDF or both NDF and GE were found to be useful for the DE prediction models. Two prediction models: DE = 16.775-0.147×NDF (R(2) = 0.73) and DE = 11.848-0.131×NDF+0.231×GE (R(2) = 0.76) were obtained. The maximum absolute difference between the in vivo DE determinations and the predicted DE values was 0.62 MJ/kg and the relative difference was 5.21%. Therefore, it was concluded that, for growing-finishing pigs, these two prediction models could be used to predict the DE content of rapeseed meal with acceptable accuracy.
منابع مشابه
Determination and Prediction of Digestible and Metabolizable Energy from the Chemical Composition of Chinese Corn Gluten Feed Fed to Finishing Pigs
Two experiments were conducted to determine the digestible energy (DE) and metabolizable energy (ME) contents of corn gluten feed (CGF) for finishing pigs and to develop equations predicting the DE and ME content from the chemical composition of the CGF samples, as well as validate the accuracy of the prediction equations. In Exp. 1, ten CGF samples from seven provinces of China were collected ...
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