: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.

Exploitation of straw resources as renewable energy source has attracted increasing interest since the present global energy crisis. Determination of both the proximate analysis and the calorific value are required before the utilisation as a biomass energy source. A total of 172 rice straw samples were collected from 17 provinces of China, and the use of near infrared reflectance spectroscopy was investigated as an alternative rapid method for proximate analysis and calorific value estimation. Different pretreatments and regression methods were examined to obtain optimum calibrations. The constructed models showed satisfactory accuracy and it was concluded that near infrared reflectance spectroscopy can be used as a rapid predictive tool for rice straw analysis.