本试验旨在利用化学成分及表观代谢能（AME）结合化学成分建立1~21日龄艾维茵肉鸡菜籽粕和棉籽粕的净能（NE）预测模型，并比较菜籽粕和棉籽粕2种样品单独建模与合并建模的预测效果。样品NE值测定采用维持净能（NEm）+沉积净能（NEp）的方法，共测定菜籽粕和棉籽粕样品各15个。NEm采用回归法测定，设自由采食及在自由采食基础上限饲30%、50%、70% 4个采食梯度。NEp采用套算法测定。每个采食梯度和菜籽粕及棉籽粕样品均设6个重复，每个重复2只鸡。试验动物为7日龄末空腹的健康艾维茵肉公鸡，平均体重为（97.3± 4.0） g。测定各个菜籽粕和棉籽粕的常规化学成分含量，并根据测定的NE、AME和化学成分进行2种样品单独与合并的线性回归分析。结果表明：菜籽粕和棉籽粕NE值的范围分别为4.72~7.22 MJ/kg DM和4.73~7.08 MJ/kg DM。AME结合化学成分建立的菜籽粕和棉籽粕NE最佳预测方程的R2分别为0.995和0.998，RSD分别为0.052和0.033 MJ/kg DM；二者合并建立的NE最佳预测方程的R2为0.995，RSD为0.052 MJ/kg DM。化学成分建立的菜籽粕和棉籽粕NE最佳预测方程的R2分别为0.973和0.985，RSD分别为0.123和0.100 MJ/kg DM，二者合并建立的NE最佳预测方程的R2为0.973，RSD为0.123 MJ/kg DM。说明测定的菜籽粕和棉籽粕NE值是较准确的；AME结合化学成分建立的NE预测模型优于只用化学成分建立的模型；菜籽粕和棉籽粕单独建模与二者合并建模效果相当，无明显差异。

This study was conducted to establish reliable prediction models for net energy (NE) values of rapeseed meals (RSM) and cottonseed meals (CSM) based on chemical composition and apparent metabolic energy (AME) for Avian broilers aged from 1 to 21 days, and to compare the predictive ability of modeling the two samples (RSM and CSM) separately and together. NE values of RSM and CSM were measured as the sum of NE values for maintenance (NEm) and for production (NEp), and 15 kinds of RSM and 15 kinds of CSM were measured. The NEm was measured by regression method with 4 feeding levels including ad libitum feeding and restricted feeding by 30%, 50% and 70%, respectively. NEp was measured by the method of substitution. Seven-day-old Avian broilers with an average weight of (97.3±4.0) g were randomly allotted into every feeding level and every sample of rapeseed meal and cottonseed meal with 6 replicates each and 2 chickens in each replicate. Proximate chemical composition of RSM and CSM was measured. The linear regression analysis was carried out between NE values, AME values and chemical composition based on the two samples separately and together. The results showed as follows: the NE values of RSM and CSM for broilers aged from 1 to 21 days were from 4.72 to 7.22 MJ/kg DM and from 4.73 to 7.08 MJ/kg DM, respectively; the R2 of the optimum regression equations for the two samples based on AME combined with chemical composition were 0.995 and 0.998, respectively; the relative standard deviations (RSD) were 0.052 and 0.033 MJ/kg DM, respectively; the R2 of the optimum regression equation for the two samples together based on AME combined with chemical composition was 0.995, and the RSD was 0.052 MJ/kg DM. The R2 of the optimum regression equations for the two samples based on the chemical composition were 0.973 and 0.985, respectively; the RSD were 0.123 and 0.100 MJ/kg DM, respectively; the R2 of the optimum regression equation for the two samples together based on the chemical composition was 0.973, and the RSD was 0.123 MJ/kg DM. The results indicate that the NE values of RSM and CSM are accurate and the reliable regression equations based on AME combined with chemical composition are better than the reliable regression equations only based on chemical composition; both of the prediction models for NE values of the two samples separately and together are reliable.［Chinese Journal of Animal Nutrition, 2011, 23（10）：1769 -1774］