| The study was conducted to investigate the feasibility of assay method of prediction feedstuffs TME and TMEn by fibre substance in drake and whether the accuracy of prediction equation was improved after feedstuffs was classified by fibre, protein or other nutrients in feedstuffs. Testing diets were formulated by substituting these feedstuffs respectively for 15-30% basal diet. One hundred four Tianfu meat drake weighing 2.5+0.15kg were taken on to assay TME, TMEn of 12 kinds of plant feedstuffs of two categories including bean meals: soybean meal (sol.)> rapeseed meal(exp.), peanut meal(exp.), sunflower meal (exp.) , cottonseed meal (sol.) and sesame meal (sol) ;by-products of cereal or distillers dried grain: hull of rice, bran of rice, wheat bran, DDGS, brewers dried grain and distillers dried grain. After determining the content of DM, ADF, NDF, CP, CF, EE, GE, Ash, St and Su of diets and feedstuffs . software SPSS11.0 was employed to analyze the regression relationship between these parameters and TME or TMEn. The application effects and limitation of the regression equations were discussed. The results showed that: Feedstuffs classified could improve accuracy of models. Fibre was the most important factor in predicting the BE of cereal by-products and distillers dried grain and beans meals (sol.or exp.) in drakes. When the Feedstuffs were not classified, CF was the best one-variate predictor. NDF, Ash and EE were the predictors of the best four-variate prediction equation. ADF was the best one-variate predictor for cereal by-products and distillers dried grain. Ash entered the two-variate prediction equation just after NDF. NDF,EE and Ash were predictors of the best three-variate prediction equation. Ash, GE, EE and NDF were predictors of the bestfour-variate prediction equation. ADF and CP were the best predictors for two-variate prediction equation of beans meals (sol.or exp.).The three - variate prediction equation was no better than the two or four- variate predition equations. ADF, GE, Ash and TS were the best preditors of four- variatepredition equation. The best prediction equations were set up as follows: By-products of cereal and distillers dried grain and beans meals.The best prediction equations of BE when feedstuffs is not classified 1) TME=13. 050**-0. 238**CF (R2=0. 859, RSD=0. 243, p<0. 01)2)TME=15.978**-0.657**Ash+0. 117**EE-0.068**NDF (R2=0.906, RSD=0.299, p<0.01)3)TMEn=12. 450**-0. 224**CF (R2=0. 830, RSD=0. 254, p<0.01)4)TMEn=15.234**-0.660**Ash+0. 123**EE-0.057**NDF (R2=0. 890, RSD-0. 311, p<0.01)The best prediction equations of TME for cereal By-products and distillers dried grain5)TME=14. 414**-0. 217**ADF (R2= 0. 940, RSD=0. 291, p<0.01)6)TME=18. 526**-0. 099"NDF-0. 670**Ash (R2=0. 998, RSD-0.071, p<0. 01)7)TME=17.933**-0.678**Ash+0.031**EE-0.089**NDF (R2=0. 998, RSD=0. 078, p<0.01)8)TME=16. 422**-0. 66l**Ash+0. 078**GE+0. 020**EE-0. 090**NDF (R2=0. 998, RSD=0. 086, p<0.01) The best prediction equations of TME of beans meals:9)TME=7. 519**+0. 133**CP-0. 396**ADF (R2=0. 950, RSD=0. 162, p<0. 01)10)TME=-11. 367**-0. 236**ADF-2. 443**Ash+2. 036**CE+0. 240**TS(R2=0.993, RSD=0.083, p<0.01) The best prediction equations of TMEn for cereal By-products and distillers dried grain11)TMEn =14. 079**-0. 211**ADF (R2=0. 939, RSD=0. 284, p<0.01)12)TMEn=18. 110**-0.613**Ash-0. 104**NDF (R2-0.997, RSD-0.084, p<0.01)13)TMEn=17.497**-0.621**Ash+0.032EE-0. 094**NDF (R2=0. 997, RSD=0. 094, p<0. 01)14)TMEn=16. 280**-0. 607**Ash+0. 063GE+0. 023EE-0. 095**NDF(R2=0. 997, RSD=0. 107, p<0.01) The best prediction equations of TMEn of... |
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