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Evaluation and application of the CPM Dairy Nutrition model

Published online by Cambridge University Press:  30 November 2007

L. O. TEDESCHI ,
W. CHALUPA ,
E. JANCZEWSKI ,
D. G. FOX ,
C. SNIFFEN ,
R. MUNSON ,
P. J. KONONOFF  and
R. BOSTON
L. O. TEDESCHI
Affiliation:
Department of Animal Science, Texas A&M University, College Station, TX 77845, USA
W. CHALUPA*
Affiliation:
School of Veterinary Medicine, University of Pennsylvania, Kennett Square, PA 19348, USA
E. JANCZEWSKI
Affiliation:
School of Veterinary Medicine, University of Pennsylvania, Kennett Square, PA 19348, USA
D. G. FOX
Affiliation:
Department of Animal Science, Cornell University, Ithaca, NY 14853, USA
C. SNIFFEN
Affiliation:
Fencrest LLC, Holderness, NH 03245, USA
R. MUNSON
Affiliation:
School of Veterinary Medicine, University of Pennsylvania, Kennett Square, PA 19348, USA
P. J. KONONOFF
Affiliation:
Department of Animal Science, University of Nebraska, Lincoln, NE 68583, USA
R. BOSTON
Affiliation:
School of Veterinary Medicine, University of Pennsylvania, Kennett Square, PA 19348, USA
*
*To whom all correspondence should be addressed. Email: [email protected]
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Summary

The Cornell-Penn-Miner (CPM) Dairy is an applied mathematical nutrition model that computes dairy cattle requirements and the supply of energy and nutrients based on characteristics of the animal, the environment and the physicochemical composition of the feeds under diverse production scenarios. The CPM Dairy was designed as a steady-state model to use rates of degradation of feed carbohydrate and protein and the rate of passage to estimate the extent of ruminal fermentation, microbial growth, and intestinal digestibility of carbohydrate and protein fractions in computing energy and protein post-rumen absorption, and the supply of metabolizable energy and protein to the animal. The CPM Dairy version 3.0 (CPM Dairy 3.0) includes an expanded carbohydrate fractionation scheme to facilitate the characterization of individual feeds and a sub-model to predict ruminal metabolism and intestinal absorption of long chain fatty acids. The CPM Dairy includes a non-linear optimization algorithm that allows for least-cost formulation of diets while meeting animal performance, feed availability and environmental restrictions of modern dairy cattle production. When the CPM Dairy 3.0 was evaluated with data of 228 individual lactating dairy cows containing appropriate information including observed dry matter intake, the linear regression between observed and model-predicted milk production values indicated the model was able to account for 79·8% of the variation. The concordance correlation coefficient (CCC) was high (rc=0·89) without a significant mean bias (0·52 kg/d; P=0·12). The accuracy estimated by the CCC was 0·997. The root of mean square error of prediction (MSEP) was 5·14 kg/d (0·16 of the observed mean) and 87·3% of the MSEP was due to random errors, suggesting little systematic bias in predicting milk production of high-producing dairy cattle. Based upon these evaluations, it was concluded the CPM Dairy 3.0 model adequately predicts milk production at the farm level when appropriate animal characterization, feed composition and feed intake are provided; however, further improvements are needed to account for individual animal variation.

Type
Modelling Animal Systems Paper
Information
The Journal of Agricultural Science , Volume 146 , Issue 2: Modelling Animal Systems , April 2008 , pp. 171 - 182
Copyright
Copyright © Cambridge University Press 2007

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References

REFERENCES

Alderman, G. (2001). A critique of the Cornell Net Carbohydrate and Protein System with emphasis on dairy cattle. 1. The rumen model. Journal of Animal and Feed Sciences 10, 1–24.CrossRefGoogle Scholar
Allen, M. S. (2000). Effects of diet on short-term regulation of feed intake by lactating dairy cattle. Journal of Dairy Science 83, 1598–1624.CrossRefGoogle ScholarPubMed
Allen, M. S., Bradford, B. J. & Harvatine, K. J. (2005). The cow as a model to study food intake regulation. Annual Review Nutrition 25, 523–547.CrossRefGoogle Scholar
Bauman, D. E. (2000). Regulation of nutrient partitioning during lactation: homeostasis and homeorhesis revisited. In Ruminant Physiology: Digestion, Metabolism, Growth and Reproduction (Ed. Cronjé, P. B.), pp. 311–328. New York: CABI Publishing.CrossRefGoogle Scholar
Bibby, J. & Toutenburg, H. (1977). Prediction and Improved Estimation in Linear Models. Berlin, Germany: John Wiley and Sons.Google Scholar
Boston, R. C., Fox, D. G., Sniffen, C. J., Janczewski, R., Munsen, R. & Chalupa, W. (2000). The conversion of a scientific model describing dairy cow nutrition and production to an industry tool: the CPM Dairy project. In Modelling Nutrient Utilization in Farm Animals (Eds McNamara, J. P., France, J. & Beever, D.), pp. 361–377. Oxford: CABI Publishing.CrossRefGoogle Scholar
Chalupa, W. & Boston, R. (2003). Development of the CNCPS and CPM models: the Sniffen affect. In Proceedings of Cornell Nutrition Conference for Feed Manufacturers, pp. 15–24. Syracuse, NY: New York State College of Agriculture and Life Sciences, Cornell University.Google Scholar
Chaves, A. V., Brookes, I. M., Waghorn, G. C., Woodward, S. L. & Burke, J. L. (2006). Evaluation of Cornell Net Carbohydrate and Protein System predictions of milk production, intake and liveweight change of grazing dairy cows fed contrast silages. Journal of Agricultural Science, Cambridge 144, 85–91.CrossRefGoogle Scholar
Dantzig, G. B. (1951). A proof of the equivalence of the programming problem and the game problem. In Activity Analysis of Production and Allocation (Ed. Koopmans, T. C.), pp. 330–335. New York, NY: John Wiley and Sons.Google Scholar
Engstrom, D. F., Mathison, G. W. & Goonewardene, L. A. (1992). Effect of β-glucan, starch, and fibre content and steam vs. dry rolling of barley grain on its degradability and utilisation by steers. Animal Feed Science and Technology 37, 33–46.CrossRefGoogle Scholar
Forbes, J. M. (2003). The multifactorial nature of food intake control. Journal of Animal Science 81, E139–E144.Google Scholar
Fox, D. G., Sniffen, C. J., O'connor, J. D., Russell, J. B. & Van Soest, P. J. (1990). The Cornell Net Carbohydrate and Protein System for Evaluating Cattle Diets, No. 34. Ithaca, NY: Cornell University Agricultural Experiment Station.Google Scholar
Fox, D. G., Tedeschi, L. O., Tylutki, T. P., Russell, J. B., Van Amburgh, M. E., Chase, L. E., Pell, A. N. & Overton, T. R. (2004). The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion. Animal Feed Science and Technology 112, 29–78.CrossRefGoogle Scholar
Garnsworthy, P. C. & Jones, G. P. (1987). The influence of body condition at calving and dietary protein supply on voluntary food intake and performance in dairy cows. Animal Production 44, 347–353.Google Scholar
Garnsworthy, P. C. & Topps, J. H. (1982). The effect of body condition of dairy cows at calving on their food intake and performance when given complete diets. Animal Production 35, 113–119.Google Scholar
Haefner, J. W. (1996). Modeling Biological Systems: Principles and Applications. New York: Chapman and Hall.CrossRefGoogle Scholar
Hall, M. B., Pell, A. N. & Chase, L. E. (1998). Characteristics of neutral detergent-soluble fiber fermentation by mixed ruminal microbes. Animal Feed Science and Technology 70, 23–29.CrossRefGoogle Scholar
Hatfield, R. D. & Weimer, P. J. (1995). Degradation characteristics of isolated and in situ cell wall lucerne pectic polysaccharides by mixed ruminal microbes. Journal of the Science of Food and Agriculture 69, 185–196.CrossRefGoogle Scholar
Hayirli, A., Grummer, R. R., Nordheim, E. V. & Crumps, P. M. (2003). Models for predicting dry matter intake of Holsteins during the prefresh transition period. Journal of Dairy Science 86, 1771–1779.CrossRefGoogle ScholarPubMed
Hintz, R. W., Mertens, D. R. & Albrecht, K. A. (1996). Effects of sodium sulfite on recovery and composition of detergent fiber and lignin. Journal of the Association of Official Analytical Chemists 79, 16–22.Google ScholarPubMed
Illius, A. W. & Jessop, N. S. (1996). Metabolic constraints on voluntary intake in ruminants. Journal of Animal Science 74, 3052–3062.CrossRefGoogle ScholarPubMed
Kolmogoroff, A. N. (1933). Sulla determinazione empirica di una legge di distribuzione. Giornale dell'Istituto Italiano degli Attuari 4, 83–91.Google Scholar
Kolver, E. S., Muller, L. D., Barry, M. C. & Penno, J. W. (1998). Evaluation and application of the Cornell Net Carbohydrate and Protein System for dairy cows fed diets based on pasture. Journal of Dairy Science 81, 2029–2039.CrossRefGoogle ScholarPubMed
Kononoff, P. J., Ivan, S. K., Matzke, W., Grant, R. J., Stock, R. A. & Klopfenstein, T. J. (2006). Milk production of dairy cows fed wet corn gluten feed during the dry period and lactation. Journal of Dairy Science 89, 2608–2617.CrossRefGoogle ScholarPubMed
Lanzas, C., Sniffen, C. J., Seo, S., Tedeschi, L. O. & Fox, D. G. (2007). A revised CNCPS feed carbohydrate fractionation scheme for formulating rations for ruminants. Animal Feed Science and Technology 136, 167–190.CrossRefGoogle Scholar
Liao, J. J. Z. (2003). An improved concordance correlation coefficient. Pharmaceutical Statistics 2, 253–261.CrossRefGoogle Scholar
Lin, L. I.-K. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268.CrossRefGoogle ScholarPubMed
Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D. & Schabenberger, O. (2006). SAS for Mixed Models. 2nd edn.Cary, NC: SAS Institute.Google Scholar
Macciotta, N. P. P., Vicario, D., Di Mauro, C. & Cappio-Borlino, A. (2004). A multivariate approach to modeling shapes of individual lactation curves of cattle. Journal of Dairy Science 87, 1092–1098.CrossRefGoogle ScholarPubMed
McDonald, P., Henderson, A. R. & Heron, S. J. E. (1991). The Biochemistry of Silage. 2nd edn.London, UK: Chalcombe Publications.Google Scholar
Mertens, D. R. (2002). Gravimetric determination of amylase-treated neutral detergent fiber in feeds with refluxing in beakers or crucibles: collaborative study. Journal of AOAC International 85(6), 1217–1240.Google ScholarPubMed
Moate, P. J., Boston, R. C., Lean, I. J. & Chalupa, W. (2006). Short communication: Further validation of the fat sub-model in the Cornell-Penn-Miner dairy model. Journal of Dairy Science 89, 1052–1056.CrossRefGoogle ScholarPubMed
Moate, P. J., Chalupa, W., Jenkins, T. G. & Boston, R. C. (2004). A model to describe ruminal metabolism and intestinal absorption of long chain fatty acids. Animal Feed Science and Technology 112, 79–105.CrossRefGoogle Scholar
Nelson, D. L. & Cox, M. M. (2005). Lehninger Principles of Biochemistry. 4th edn.New York, NY: W.H. Freeman and Company.Google Scholar
Neter, J., Kutner, M. H., Nachtsheim, C. J. & Wasserman, W. (1996). Applied Linear Statistical Models. 4th edn.Boston: McGraw-Hill Publishing Co.Google Scholar
Newman, S., Lynch, T. & Plummer, A. A. (2000). Success and failure of decision support systems: learning as we go. Journal of Animal Science 77, 1–12.CrossRefGoogle Scholar
Offner, A. & Sauvant, D. (2004). Comparative evaluation of the Molly, CNCPS, and LES rumen models. Animal Feed Science and Technology 112, 107–130.CrossRefGoogle Scholar
Overton, T. R. & Waldron, M. R. (2004). Nutritional management of transition dairy cows: strategies to optimize metabolic health. Journal of Dairy Science 87, E105–E119.CrossRefGoogle Scholar
Pitt, R. E., Van Kessel, J. S., Fox, D. G., Pell, A. N., Barry, M. C. & Van Soest, P. J. (1996). Prediction of ruminal volatile fatty acids and pH within the net carbohydrate and protein system. Journal of Animal Science 74, 226–244.CrossRefGoogle ScholarPubMed
Roseler, D. K., Fox, D. G., Chase, L. E., Pell, A. N. & Stone, W. C. (1997). Development and evaluation of equations for prediction of intake for lactating Holstein dairy cows. Journal of Dairy Science 80, 878–893.CrossRefGoogle ScholarPubMed
Ruiz, R., Albrecht, G. L., Tedeschi, L. O., Jarvis, G., Russell, J. B. & Fox, D. G. (2001). Effect of monensin on the performance and nitrogen utilization of lactating dairy cows consuming fresh forage. Journal of Dairy Science 84, 1717–1727.CrossRefGoogle ScholarPubMed
Ruiz, R., Tedeschi, L. O., Marini, J. C., Fox, D. G., Pell, A. N., Jarvis, G. & Russell, J. B. (2002). The effect of a ruminal nitrogen (N) deficiency in dairy cows: evaluation of the Cornell net carbohydrate and protein system ruminal N deficiency adjustment. Journal of Dairy Science 85, 2986–2999.CrossRefGoogle ScholarPubMed
Sniffen, C. J., O'Connor, J. D., Van Soest, P. J., Fox, D. G. & Russell, J. B. (1992). A net carbohydrate and protein system for evaluating cattle diets: II. Carbohydrate and protein availability. Journal of Animal Science 70, 3562–3577.CrossRefGoogle ScholarPubMed
St-Pierre, N. R. (2001). Integrating quantitative findings from multiple studies using mixed models methodology. Journal of Dairy Science 84, 741–755.CrossRefGoogle Scholar
Stone, W. C. (1996). Applied topics in dairy cattle nutrition: 1. Soyhulls as either a forage or concentrate replacement in early lactation Holstein dairy cattle, 2. Evaluation of the Cornell Net Carbohydrate and Protein System's metabolizable protein requirement as supply in Holstein dairy cattle, 3. In vitro effects of lipids on fermentation systems. Ph.D. Dissertation, Cornell University.Google Scholar
Tedeschi, L. O. (2006). Assessment of the adequacy of mathematical models. Agricultural Systems 89, 225–247.CrossRefGoogle Scholar
Tedeschi, L. O., Fox, D. G., Chase, L. E. & Wang, S. J. (2000). Whole-herd optimization with the Cornell Net Carbohydrate and Protein System. I. Predicting feed biological values for diet optimization with linear programming. Journal of Dairy Science 83, 2139–2148.CrossRefGoogle Scholar
Tedeschi, L. O., Fox, D. G., Pell, A. N., Lanna, D. P. D. & Boin, C. (2002). Development and evaluation of a tropical feed library for the Cornell Net Carbohydrate and Protein System model. Scientia Agricola 59, 1–18.CrossRefGoogle Scholar
Tedeschi, L. O., Fox, D. G., Sainz, R. D., Barioni, L. G., Medeiros, S. R. & Boin, C. (2005). Using mathematical models in ruminant nutrition. Scientia Agricola 62, 76–91.CrossRefGoogle Scholar
Tedeschi, L. O., Seo, S., Fox, D. G. & Ruiz, R. (2006). Accounting for energy and protein reserve changes in predicting diet-allowable milk production in cattle. Journal of Dairy Science 89, 4795–4807.CrossRefGoogle ScholarPubMed
Tylutki, T. P. (2002). Improving herd nutrient management on dairy farms: (1) Daily milk production variance in high producing cows as an indicator of diet nutrient balance. (2) On-farm six sigma quality management of diet nutrient variance. (3) Feedstuff variance on a commercial dairy and the predicted associated milk production variance. (4) A model to predict cattle nitrogen and phosphorus excretion with alternative herd feed programs. (5) Accounting for uncertainty in ration formulation. Ph.D. Dissertation, Cornell University.Google Scholar
Tylutki, T. P., Fox, D. G. & McMahon, M. (2004). Implementation of nutrient management planning on a dairy farm. The Professional Animal Scientist 20, 58–65.CrossRefGoogle Scholar
Van Soest, P. J. (1994). Nutritional Ecology of the Ruminant. 2nd edn.Ithaca, NY: Comstock Publishing Associates.CrossRefGoogle Scholar
Van Soest, P. J., Van Amburgh, M. E., Robertson, J. B. & Knaus, W. F. (2005). Validation of the 2·4 times lignin factor for ultimate extent of NDF digestion, and curve peeling rate of fermentation curves into pools. In Proceedings of Cornell Nutrition Conference for Feed Manufacturers, pp. 139–150. Syracuse, NY: New York State College of Agriculture and Life Sciences, Cornell University.Google Scholar
Van Soest, P. J., Van Amburgh, M. E. & Tedeschi, L. O. (2000). Rumen balance and rates of fiber digestion. In Proceedings of Cornell Nutrition Conference for Feed Manufacturers, pp. 150–166. Rochester, NY: New York State College of Agriculture and Life Sciences, Cornell University.Google Scholar
Zhou, J. L., Tits, A. L. & Lawrence, C. T. (1997). User's Guide for FFSQP version 3.7: A FORTRAN Code for Solving Constrained Nonlinear (Minimax) Optimization Problems, Generating Iterates Satisfying All Inequality and Linear Constraints, p. 46. University of Maryland, College Park, MD: Electrical Engineering Department and Institute for Systems Research.Google Scholar