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Evaluation of Dry Matter Intake and Average Daily Gain Predicted by the Cornell Net Carbohydrate and Protein System in Crossbred Growing Bulls Kept in a Traditionally Confined Feeding System in China

  • Du, Jinping (College of Animal Sciences, Yangtze University) ;
  • Liang, Yi (State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University) ;
  • Xin, Hangshu (College of Animal Science and Technology, Northeast Agricultural University) ;
  • Xue, Feng (State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University) ;
  • Zhao, Jinshi (State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University) ;
  • Ren, Liping (State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University) ;
  • Meng, Qingxiang (State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University)
  • Received : 2010.02.12
  • Accepted : 2010.04.26
  • Published : 2010.11.01

Abstract

Two separate animal trials were conducted to evaluate the coincidence of dry matter intake (DMI) and average daily gain (ADG) predicted by the Cornell Net Carbohydrate and Protein System (CNCPS) and observed actually in crossbred growing bulls kept in a traditionally confined feeding system in China. In Trial 1, 45 growing Simmental${\times}$Mongolia crossbred F1 bulls were assigned to three treatments (T1-3) with 15 animals in each treatment. Trial 2 was conducted with 60 Limousin${\times}$Fuzhou crossbred F2 bulls allocated to 4 treatments (t1-4). All of the animals were confined in individual stalls. DMI and ADG for each bull were measured as a mean of each treatment. All of the data about animals, environment, management and feeds required by the CNCPS model were collected, and model predictions were generated for animals on each treatment. Subsequently, model-predicted DMI and ADG were compared with the actually recorded results. In the three treatments in Trial 1, 93.3, 80.0 and 73.3% of points fell within the range from -0.4 to 0.4 kg/d for DMI mean bias; similarly, in the four treatments in Trial 2, about 86.7, 73.3, 73.3 and 80.0% of points fell within the same range. These results indicate that the CNCPS model can accurately predict DMI of crossbred bulls in the traditionally confined feeding system in China. There were no significant differences between predicted and observed ADG for T1 (p = 0.06) and T2 (p = 0.09) in Trial 1, and for t1 (p = 0.07), t2 (p = 0.14) and t4 (p = 0.83) in Trial 2. However, significant differences between predicted and observed ADG values were observed for T3 in Trial 1 (p<0.01) and for t3 in Trial 2 (p = 0.04). By regression analysis, a statistically different value of intercept from zero for the regression equation of DMI (p<0.01) or an identical value of ADG (p = 0.06) were obtained, whereas the slopes were significantly different (p<0.01) from unity for both DMI and ADG. Additionally, small root mean square error (RMSE) values were obtained for the unbiased estimator of the two variances (DMI and ADG). Thus, the present results indicated that the CNCPS model can give acceptable estimates of DMI and ADG of crossbred growing bulls kept in a traditionally confined feeding system in China.

Keywords

References

  1. Beermann, D. H. and D. G. Fox. 1998. Use of feed and water by livestock in the United States. Cornell CALS news, May 1995.
  2. Bibby, J. and H. Toutenburg. 1997. Prediction and improved estimation in linear models. John Wiley & Sons Publishers, Berlin, Germany.
  3. Forbes, J. M. 1996. Integration of regulatory signals controlling forage intake in ruminants. J. Anim. Sci. 74:3029-3035.
  4. Fox, D. G., C. J. Sniffen, J. D. O’Connor, J. B. Russell and P. J.Van Soest. 1992. A net carbohydrate and protein system for evaluating cattle diets: III. Cattle requirements and diet adequacy. J. Anim. Sci. 70:3578-3596.
  5. Fox, D. G., L. O. Tedeschi, T. P. Tylutki, J. B. Russell, M. E. VanAmburgh, L. E. Chase, A. N. Pell and T. R. Overton. 2004.The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion. Anim. Feed Sci. Technol. 112:29-78. https://doi.org/10.1016/j.anifeedsci.2003.10.006
  6. Fox, D. G., T. P. Tylutki, L. O. Tedeschi, M. E. Van Amburgh, L. E.Chase, A. N. Pell, T. R. Overton and J. B. Russell. 2003. The net carbohydrate and protein system for evaluating herd nutrition and nutrient excretion. CNCPS version 5.0, Model Documentation. Ithaca, NY, USA.
  7. Hall, M. B. and C. Herejk. 2001. Differences in yields of microbial crude protein from in vitro fermentation of carbohydrates. J. Dairy Sci. 84:2486-2493. https://doi.org/10.3168/jds.S0022-0302(01)74699-1
  8. Kolver, E. S., M. C. Barry, J. W. Penno and L. D. Muller. 1996.Evaluation of the Cornell net carbohydrate and protein system for dairy cows fed pasture-based diets. Proc. NZ Soc. Anim. Prod. 56:251-254.
  9. Lanzas, C., C. J. Sniffen, S. Seo, L. O. Tedeschi and D. G. Fox. 2007. A revised CNCPS feed carbohydrate fractionation scheme for formulating rations for ruminants. Anim. Feed Sci. Technol. 136:167-190. https://doi.org/10.1016/j.anifeedsci.2006.08.025
  10. Mayer, D. G. and D. G. Butler. 1993. Statistical validation. Ecol. Model. 68:21-32. https://doi.org/10.1016/0304-3800(93)90105-2
  11. Mitchell, P. L. 1997. Misuse of regression for empirical validation of model. Agric. Syst. 54:313-326. https://doi.org/10.1016/S0308-521X(96)00077-7
  12. Mitchell, P. L. and J. E. Sheehy. 1997. Comparison of predictions and observations to assess model performance: a method of empirical validation. In: Applications of Systems Approaches at the Field Level (Ed. M. J. Kropff, P. S. Teng, P. K. Aggarwal, J. Bouma, B. A. M. Bouman, J. W. Jones, H. H. Van Laar). Volume 2. Kluwer Academic Publishers, Boston, USA. pp. 437-451.
  13. Molina, D. O., I. Matamoros, Z. Almeida, L. Tedeschi and A. N.Pell. 2004. Evaluation of the dry matter intake predictions of the Cornell Net carbohydrate and protein system with Holstein and dual-purpose lactating cattle in the tropics. Anim. Feed Sci. Technol. 114:261-278. https://doi.org/10.1016/j.anifeedsci.2003.11.010
  14. National Research Council. 2000. Nutrient requirements of beef cattle, Seventh revised Ed. Updated. National Academy Press, Washington, DC, USA.
  15. Offner, A. and D. Sauvant. 2004. Comparative evaluation of the MOLLY, CNCPS, and LES rumen models. Anim. Feed Sci. Technol. 112:107-130. https://doi.org/10.1016/j.anifeedsci.2003.10.008
  16. Sniffen, C. J., J. D. O’Connor, P. J. Van Soest, D. G. Fox and J. B. Russell. 1992. A net carbohydrate and protein system for evaluating cattle diets: II. Carbohydrate and protein availability. J. Anim. Sci. 70:3562-3577.
  17. Tedeschi, L. O. 2006. Assessment of the adequacy of mathematical models. Agrc. Syst. 89:225-247. https://doi.org/10.1016/j.agsy.2005.11.004
  18. Tedeschi, L. O. and D. G. Fox. 2001. Application of the net Cornell carbohydrate and protein system for tropical conditions. Revista Corpoica. 3(2):1-10.
  19. Tedeschi, L. O., D. G. Fox, A. N. Pell, D. P. D. Lanna and C. Boin.2002. Development and evaluation of a tropic feed library for the Cornell net carbohydrate and protein system model. Sci. Agric. 59(1):1-18. https://doi.org/10.1590/S0103-90162002000100001
  20. Tedeschi, L. O., D. G. Fox and J. B. Russell. 2000. Accounting for the effects of a ruminal nitrogen deficiency within the structure of the Cornell net carbohydrate and protein system. J. Anim. Sci. 78:1648-1658.
  21. Zhao, J. S., Z. M. Zhou, L. P. Ren, Y. Q. Xiong, J. P. Du and Q. X.Meng. 2008. Evaluation of dry matter intake and daily weight gain predictions of the Cornell net carbohydrate and protein system with local breeds of beef cattle in China. Anim. Feed Sci. Technol. 142:231-246. https://doi.org/10.1016/j.anifeedsci.2007.08.008