JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Prediction of Dry Matter Intake in Lactating Holstein Dairy Cows Offered High Levels of Concentrate
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Prediction of Dry Matter Intake in Lactating Holstein Dairy Cows Offered High Levels of Concentrate
Rim, J.S.; Lee, S.R.; Cho, Y.S.; Kim, E.J.; Kim, J.S.; Ha, Jong K.;
  PDF(new window)
 Abstract
Accurate estimation of dry matter intake (DMI) is a prerequisite to meet animal performance targets without penalizing animal health and the environment. The objective of the current study was to evaluate some of the existing models in order to predict DMI when lactating dairy cows were offered a total mixed ration containing a high level of concentrates and locally produced agricultural by-products. Six popular models were chosen for DMI prediction (Brown et al., 1977; Rayburn and Fox, 1993; Agriculture Forestry and Fisheries Research Council Secretariat, 1999; National Research Council (NRC), 2001; Cornell Net Carbohydrate and Protein System (CNCPS), Fox et al., 2003; Fuentes-Pila et al., 2003). Databases for DMI comparison were constructed from two different sources: i) 12 commercial farm investigations and ii) a controlled dairy cow experiment. The model evaluation was performed using two different methods: i) linear regression analysis and ii) mean square error prediction analysis. In the commercial farm investigation, DMI predicted by Fuentes-Pila et al. (2003) was the most accurate when compared with the actual mean DMI, whilst the CNCPS prediction showed larger mean bias (difference between mean predicted and mean observed values). Similar results were observed in the controlled dairy cow experiment where the mean bias by Fuentes-Pila et al. (2003) was the smallest of all six chosen models. The more accurate prediction by Fuentes-Pila et al. (2003) could be attributed to the inclusion of dietary factors, particularly fiber as these factors were not considered in some models (i.e. NRC, 2001; CNCPS (Fox et al., 2003)). Linear regression analysis had little meaningful biological significance when evaluating models for prediction of DMI in this study. Further research is required to improve the accuracy of the models, and may recommend more mechanistic approaches to investigate feedstuffs (common to the Asian region), animal genotype, environmental conditions and their interaction, as the majority of the models employed are based on empirical approaches.
 Keywords
High Chromium Yeast;Immune Response;Some Blood Parameters;Lambs;
 Language
English
 Cited by
1.
건물 및 영양소 섭취량 제한이 농후사료 급여 비율이 높은 착유우의 생산성에 미치는 영향,조영석;임종수;장원석;김명화;이상락;

Journal of Animal Science and Technology, 2009. vol.51. 1, pp.33-38 crossref(new window)
 References
1.
Agricultural Research Council. 1980. The Nutrient Requirements of Ruminant Livestock. Commonwealth Agricultural Bureaux, Farnham Royal, UK.

2.
Agriculture Forestry and Fisheries Research Council Secretariat. 1999. Japanese Feeding Standard for Dairy Cattle. Central Association of Livestock Industry, Ministry of Agriculture, Forestry and Fisheries of Japan, Tokyo.

3.
Association of Official Analytical Chemists. 1990. Official methods of analysis. 15th ed., Arlington, VA.

4.
Beever, D. E. 1993. Characterization of forage: Appraisal of current practice and future opportunities. In Recent Developments in Ruminant Nutrition 3 (Ed. P. C. Garnsworthy and D. J. A. Cole). Nottingham University Press, Nottingham, UK. pp. 113-127.

5.
Brown, C. A., P. T. Chandler and J. B. Holter. 1977. Development of predictive equations for milk yield and dry matter intake in lactating cows. J. Dairy Sci. 60:1739-1754. crossref(new window)

6.
Chaves, A. V., I. M. Brookes, G. C. Waghorn, S. L. Woodward and J. L. Burke. 2006. Evaluation of Cornell Net Carbohydrate and Protein System predictions of milk production, intake and liveweight change of grazing dairy cows fed contrast silages. J. Agric. Sci. 144:85-91. crossref(new window)

7.
Chiou, P. W. S., C. H. Chuang, B. Yu, S. Y. Hwang and C. R. Chen. 2006. Application of Cornell net carbohydrate and protein system to lactating cows in Taiwan. Asian-Aust. J. Anim. Sci. 19:857-864.

8.
Dhanoa, M. S., S. J. Lister, J. France and R. J. Barnes. 1999. Use of mean square prediction error analysis and reproducibility measures to study near infrared calibration equation performance. J. Near Infrared Spectrosc. 7:133-143. crossref(new window)

9.
Ellis, J. L., F. Qiao and J. P. Cant. 2006. Prediction of dry matter intake throughout lactation in a dynamic model of dairy cow performance. J. Dairy Sci. 89:1558-1570. crossref(new window)

10.
Forbes, J. M. 1995. The voluntary food intake and diet selection of farm animals. 2nd ed., Commonwealth Agricultural Bureaux, Slough, UK.

11.
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: model documentation. Animal Science Department, Cornell University, Ithaca, NY, USA.

12.
Fuentes-Pila, J., M. Ibanez, J. M. De Miguel and D. K. Beede. 2003. Predicting average feed intake of lactating Holstein cows fed totally mixed rations. J. Dairy Sci. 86:309-323. crossref(new window)

13.
Ingvartsen, K. L. 1994. Models of voluntary food intake in cattle. Livest. Prod. Sci. 39:19-38. crossref(new window)

14.
Kohn, R. A., K. F. Kalscheur and M. Hanigan. 1998. Evaluation of models for balancing the protein requirements of dairy cows. J. Dairy Sci. 81:3402-3414. crossref(new window)

15.
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. crossref(new window)

16.
Licitra, G., T. M. Hernandez and P. J. Van Soest. 1996. Standardization of procedures for nitrogen fractionation of ruminant feeds. Anim. Feed Sci. Technol. 57:347-358. crossref(new window)

17.
Martin, O. and D. Sauvant. 2007. Dynamic model of the lactating dairy cow metabolism. Anim. 1:1143-1166.

18.
Mazumder, M. A. R. and H. Kumagai. 2006. Analyses of factors affecting dry matter intake of lactating dairy cows. Anim. Sci. J. 77:53-62. crossref(new window)

19.
Mertens, D. R. 1994. Regulation of forage intake. In: Forage Quality, Evaluation, and Utilization, (Ed. G. C. Fahey Jr., M. Collins, D. R. Mertens and L. E. Moser) American Society of Agronomy, Crop Science Society of America, Soil Science Society of America Madison, WI, USA. pp. 450-493.

20.
Mertens, D. R. 1997. Creating a system for meeting the fiber requirements of dairy cows. J. Dairy Sci. 80:1463-1481. crossref(new window)

21.
Mertens, D. R. 2002. Gravimetric determination of amylasetreated neutral detergent fiber in feeds with refluxing in beakers or crucibles: collaborative study. J. AOAC Int. 85:1217-1240.

22.
Ministry of Agriculture and Forestry. 2002. Korean feeding standard for dairy cattle. National Livestock Research Institute, Rural Development Administration, Ministry of Agriculture and Forestry, Republic of Korea.

23.
Mitchell, P. L. 1997. Misuse of regression for empirical validation of models. Agric. Syst. 54:313-326. crossref(new window)

24.
National Research Council. 2001. Nutrient requirements of dairy cattle. 7th revised ed. National Academy Press, Washington, DC.

25.
Rayburn, E. B. and D. G. Fox. 1993. Variation in neutral detergent fiber intake of Holstein cows. J. Dairy Sci. 76:544-554. crossref(new window)

26.
Roseler, D. K., D. G. Fox, L. E. Chase, A. N. Pell and W. C. Stone. 1997. Development and evaluation of equations for prediction of feed intake for lactating Holstein dairy cows. J. Dairy Sci. 80:878-893. crossref(new window)

27.
SAS Institute. 2001. SAS User's Guide, Ver. 8.02. SAS Institute Inc., Cary, NC, USA.

28.
St-Pierre, N. R. 2001. Invited review: Integrating quantitative findings from multiple studies using mixed model methodology. J. Dairy Sci. 84:741-755. crossref(new window)

29.
St-Pierre, N. R. and C. S. Thraen. 1999. Animal grouping strategies, sources of variation, and economic factors affecting nutrient balance on dairy farms. J. Anim. Sci. 77 (Suppl. 2):72-83.

30.
Sunagawa, K., T. Ooshiro, N. Nakamura, Y. Ishii, I. Nagamine and A. Shinjo. 2007. Physiological factors depressing feed intake and saliva secretion in goats fed on dry forage. Asian-Aust. J. Anim. Sci. 20:60-69.

31.
Waldo, D. R. 1986. Effect of forage quality on intake and forage concentrate interactions. J. Dairy Sci. 69:617-631. crossref(new window)