Evaluation and application of the CPM Dairy Nutrition model

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.

[1]  Luis Gustavo Barioni,et al.  Mathematical models in ruminant nutrition , 2005 .

[2]  P. V. Soest,et al.  A net carbohydrate and protein system for evaluating cattle diets: II. Carbohydrate and protein availability. , 1992, Journal of animal science.

[3]  R. Boston,et al.  A model to describe ruminal metabolism and intestinal absorption of long chain fatty acids , 2004 .

[4]  Luis O. Tedeschi,et al.  Development and evaluation of a tropical feed library for the Cornell Net Carbohydrate and Rrotein System model , 2002 .

[5]  Luis Orlindo Tedeschi,et al.  Assessment of the adequacy of mathematical models , 2006 .

[6]  T. Tylutki,et al.  Implementation of Nutrient Management Planning on a Dairy Farm , 2004 .

[7]  A. F. M. Smith,et al.  Prediction and Improved Estimation in Linear Models , 1978 .

[8]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[9]  D. Bauman,et al.  Regulation of nutrient partitioning during lactation: homeostasis and homeorhesis revisited. , 2000 .

[10]  D. Vicario,et al.  A multivariate approach to modeling shapes of individual lactation curves in cattle. , 2004, Journal of dairy science.

[11]  A. Chaves,et al.  Evaluation of Cornell Net Carbohydrate and Protein System predictions of milk production, intake and liveweight change of grazing dairy cows fed contrast silages , 2006, The Journal of Agricultural Science.

[12]  E. Kebreab,et al.  A critique of the Cornell Net Carbohydrate and Protein System with emphasis on dairy cattle. 3. The requirements model , 2001 .

[13]  L O Tedeschi,et al.  Effect of monensin on the performance and nitrogen utilization of lactating dairy cows consuming fresh forage. , 2001, Journal of dairy science.

[14]  D. E. Beever,et al.  Modelling Nutrient Utilization in Farm Animals , 2000 .

[15]  M. J. Hatcher,et al.  Modeling Biological Systems: Principles and Applications , 1997 .

[16]  A. Pell,et al.  Prediction of ruminal volatile fatty acids and pH within the net carbohydrate and protein system. , 1996, Journal of animal science.

[17]  P. Garnsworthy,et al.  The influence of body condition at calving and dietary protein supply on voluntary food intake and performance in dairy cows , 1987 .

[18]  T. Overton,et al.  Nutritional Management of Transition Dairy Cows: Strategies to Optimize Metabolic Health , 2004 .

[19]  A. Illius,et al.  Metabolic constraints on voluntary intake in ruminants. , 1996, Journal of animal science.

[20]  O. H. Brownlee,et al.  ACTIVITY ANALYSIS OF PRODUCTION AND ALLOCATION , 1952 .

[21]  L. D. Muller,et al.  Evaluation and application of the Cornell Net Carbohydrate and Protein System for dairy cows fed diets based on pasture. , 1998, Journal of dairy science.

[22]  Jason J. Z. Liao,et al.  An improved concordance correlation coefficient , 2003 .

[23]  A. Pell,et al.  Characteristics of neutral detergent-soluble fiber fermentation by mixed ruminal microbes , 1998 .

[24]  A. Lehninger.  Principles of Biochemistry , 1984 .

[25]  P. Cronje,et al.  Ruminant Physiology: Digestion, Metabolism, Growth and Reproduction , 2000 .

[26]  J. Marini,et al.  The effect of a ruminal nitrogen (N) deficiency in dairy cows: evaluation of the cornell net carbohydrate and protein system ruminal N deficiency adjustment. , 2002, Journal of dairy science.

[27]  L O Tedeschi,et al.  Whole-herd optimization with the Cornell Net Carbohydrate and Protein System. I. Predicting feed biological values for diet optimization with linear programming. , 2000, Journal of dairy science.

[28]  Raymond C. Boston,et al.  The Conversion of a Scientific Model Describing Dairy Cow Nutrition and Production to an Industry Tool: the CPM Dairy Project , 2000 .

[29]  M. Allen.  Effects of diet on short-term regulation of feed intake by lactating dairy cattle. , 2000, Journal of dairy science.

[30]  W. D. Ray.  Prediction and Improved Estimation in Linear Models , 1979 .

[31]  T. Lynch,et al.  Success and failure of decision support systems: Learning as we go , 2000 .

[32]  J. Clegg,et al.  Gravimetric Determination of Amylase-Treated Neutral Detergent Fiber in Feeds with Refluxing in Beakers or Crucibles : Collaborative Study , 2022 .

[33]  Short communication: Further validation of the fat sub-model in the Cornell-Penn-Miner Dairy model. , 2006, Journal of dairy science.

[34]  M. E. Van Amburgh,et al.  The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion , 2004 .

[35]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[36]  S. Wulff.  SAS for Mixed Models , 2007 .

[37]  J. M. Forbes.  The multifactorial nature of food intake control , 2003 .

[38]  A. L. Tits,et al.  User's Guide for FSQP Version 3.0c: A FORTRAN Code for Solving Constrained Nonlinear (Minimax) Optimization Problems, Generating Iterates Satisfying All Inequality and Linear Constraints , 1992 .

[39]  L. Tedeschi,et al.  Accounting for energy and protein reserve changes in predicting diet-allowable milk production in cattle. , 2006, Journal of dairy science.

[40]  P. V. Soest.  Nutritional Ecology of the Ruminant , 1994 .

[41]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[42]  A. Pell,et al.  Development and evaluation of equations for prediction of feed intake for lactating Holstein dairy cows. , 1997, Journal of dairy science.

[43]  A. Offner,et al.  Comparative evaluation of the Molly, CNCPS, and LES rumen models , 2004 .

[44]  AN Kolmogorov-Smirnov,et al.  Sulla determinazione empírica di uma legge di distribuzione , 1933 .

[45]  P. Garnsworthy,et al.  The effect of body condition of dairy cows at calving on their food intake and performance when given complete diets , 1982 .

[46]  James W. Haefner,et al.  Modeling Biological Systems , 1996, Springer US.

[47]  J. Cohen,et al.  Modeling Biological Systems. Principles and Applications , 1997 .

[48]  M. Allen,et al.  The cow as a model to study food intake regulation. , 2005, Annual review of nutrition.

[49]  L. Goonewardene,et al.  Effect of β-glucan, starch, and fibre content and steam vs. dry rolling of barley grain on its degradability and utilisation by steers , 1992 .

[50]  P. Weimer,et al.  Degradation characteristics of isolated and in situ cell wall lucerne pectic polysaccharides by mixed ruminal microbes , 1995 .

[51]  D. Mertens,et al.  Effects of sodium sulfite on recovery and composition of detergent fiber and lignin. , 1996, Journal of AOAC International.

[52]  L. Tedeschi,et al.  A revised CNCPS feed carbohydrate fractionation scheme for formulating rations for ruminants , 2007 .

[53]  N R St-Pierre,et al.  Invited review: Integrating quantitative findings from multiple studies using mixed model methodology. , 2001, Journal of dairy science.

[54]  Milk production of dairy cows fed wet corn gluten feed during the dry period and lactation. , 2006, Journal of dairy science.

[55]  E. Nordheim,et al.  Models for predicting dry matter intake of Holsteins during the prefresh transition period. , 2003, Journal of dairy science.

[56]  P. Mcdonald,et al.  The biochemistry of silage , 1981 .

[57]  W. Knaus,et al.  Validation of the 2,4 times lignin factor for ultimate extent of NDF digestion, and curve peeling rate of fermentation curves into pools. , 2008 .