1
|
Martineau R, Ouellet DR, Pellerin D, Firkins JL, Hanigan MD, White RR, LaPierre PA, Van Amburgh ME, Lapierre H. Ability of three dairy feed evaluation systems to predict postruminal outflows of nitrogenous compounds in dairy cows: A meta-analysis. J Dairy Sci 2023; 106:8583-8610. [PMID: 37683889 DOI: 10.3168/jds.2022-23215] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 05/15/2023] [Indexed: 09/10/2023]
Abstract
Adequate prediction of postruminal outflow of protein fractions is the starting point for the determination of metabolizable protein supply in dairy cows. The objective of this meta-analysis was to compare the performance of 3 dairy feed evaluation systems (National Research Council [NRC], Cornell Net Protein and Carbohydrate System [CNCPS], and National Academies of Sciences, Engineering and Medicine [NASEM]) to predict outflows (g/d) of nonammonia nitrogren (NAN), microbial N (MiN), and nonammonia nonmicrobial N (NANMN). Predictions of rumen degradabilities (% of nutrient) of protein (RDP), NDF, and starch were also evaluated. The data set included 1,294 treatment means from 312 digesta flow studies. The 3 feed evaluation systems were compared using the concordance correlation coefficient (CCC), the ratio of root mean square prediction error (RMSPE) on standard deviation of observed values (RSR), and the slope between observed and predicted values. Mean and linear biases were deemed biologically relevant and are discussed if higher than a threshold of 5% of the mean of observed values. The comparisons were done on observed values adjusted or not for the study effect; the adjustment had a small effect on the mean bias but the linear bias reflected a response to a dietary change rather than absolute predictions. For the absolute predictions of NAN and MiN, CNCPS had the best-fit statistics (8% greater CCC; 6% lower RMSPE) without any bias; NRC and NASEM underpredicted NAN and MiN, and NASEM had an additional linear bias indicating that the underprediction of MiN increased at increased predictions. For NANMN, fit statistics were similar among the 3 feed evaluation systems with no mean bias; however, the linear bias with NRC and CNCPS indicated underprediction at low predictions and overprediction at elevated predictions. On average, the CCC were smaller and RSR ratios were greater for MiN versus NAN indicating increased prediction errors for MiN. For NAN responses to a dietary change, CNCPS also had the best predictions, although the mean bias with NASEM was not biologically relevant and the 3 feed evaluation systems did not present a linear bias. However, CNCPS, but not the 2 other feed evaluation systems, presented a linear bias for MiN, with responses being overpredicted at increased predictions. For NANMN, responses were overpredicted at increased predictions for the 3 feed evaluation systems, but to a lesser extent with NASEM. The site of sampling had an effect on the mean bias of MiN and NANMN in the 3 feed evaluation systems. The mean bias of MiN was higher in omasal than duodenal studies in the 3 feed evaluation systems (from 55 to 61 g/d) and this mean bias was twice as large when 15N labeling was used as a microbial marker compared with purines. Such a difference was not observed for duodenal studies. The reasons underlying these systematic differences are not clear as the type of measurements used in the current meta-analysis does not allow to delineate if one site or one microbial marker is yielding the "true" postruminal N outflows. Rumen degradabilities of protein was underpredicted with CNCPS, and RDP responses to a dietary change was underpredicted by the 3 feed evaluation systems with increased RDP predictions. Rumen degradability of NDF was underpredicted and had poor fit statistics for NASEM compared with CNCPS. Fit statistics were similar between CNCPS and NASEM for rumen degradability of starch, but with an underprediction of the response with NASEM and absolute values being overpredicted with CNCPS. Multivariate regression analyses showed that diet characteristics were correlated with prediction errors of N outflows in each feed evaluation system. Globally, compared with NAN and NANMN, residuals of MiN were correlated with several moderators in the 3 feed evaluation systems reflecting the complexity to measure and model this outflow. In addition, residuals of NANMN were correlated positively with RDP suggesting an overestimation of this parameter. In conclusion, although progress is still to be made to improve equations predicting postruminal N outflows, the current feed evaluation systems provide sufficient precision and accuracy to predict postruminal outflows of N fractions.
Collapse
Affiliation(s)
- R Martineau
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, QC, Canada, J1M 0C8.
| | - D R Ouellet
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, QC, Canada, J1M 0C8
| | - D Pellerin
- Department of Animal Science, Laval University, Québec, QC, Canada, G1V 0A6
| | - J L Firkins
- Department of Animal Sciences, The Ohio State University, Columbus, OH 43210
| | - M D Hanigan
- Department of Dairy Science, Virginia Tech, Blacksburg, VA 24060
| | - R R White
- Department of Dairy Science, Virginia Tech, Blacksburg, VA 24060
| | - P A LaPierre
- Department of Animal Science, Cornell University, Ithaca, NY 14850
| | - M E Van Amburgh
- Department of Animal Science, Cornell University, Ithaca, NY 14850
| | - H Lapierre
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, QC, Canada, J1M 0C8
| |
Collapse
|
2
|
Daley V, Armentano L, Hanigan M. Models to predict milk fat concentration and yield of lactating dairy cows: A meta-analysis. J Dairy Sci 2022; 105:8016-8035. [DOI: 10.3168/jds.2022-21777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 06/07/2022] [Indexed: 11/19/2022]
|
3
|
Pacheco RF, Machado DS, Viana AFP, Teixeira JS, Milani L. Comparison of the effects of slow-release urea vs conventional urea supplementation on some finishing cattle parameters: A meta-analysis. Livest Sci 2021. [DOI: 10.1016/j.livsci.2021.104549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
4
|
Morales AG, Vibart RE, Li MM, Jonker A, Pacheco D, Hanigan MD. Evaluation of Molly model predictions of ruminal fermentation, nutrient digestion, and performance by dairy cows consuming ryegrass-based diets. J Dairy Sci 2021; 104:9676-9702. [PMID: 34127259 DOI: 10.3168/jds.2020-19740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 04/20/2021] [Indexed: 11/19/2022]
Abstract
Several studies have been conducted to improve grazing management and supplementation in pasture-based systems. However, it is necessary to develop tools that integrate the available information linking the representation of biological processes with animal performance for use in decision making. The objective of this study was to evaluate the precision and accuracy of the Molly cow model predictions of ruminal fermentation, nutrient digestion, and animal performance by cows consuming pasture-based diets to identify model strengths and weaknesses, and to derive new digestive parameters when relevant. Model modifications for adipose tissue, protein synthesis in lean body mass and viscera representation were included. Data used for model evaluations were collected from 25 publications containing 115 treatment means sourced from studies conducted with lactating dairy cattle. The inclusion criteria were that diets contained ≥45% perennial ryegrass (Lolium perenne L.), and that dry matter intake, dietary ingredient composition, and nutrient digestion observations were reported. Animal performance and N excretion variables were also included if they were reported. Model performance was assessed before and after model reparameterization of selected digestive parameters, global sensitivity analysis was conducted after reparameterization, and a 5-fold cross evaluation was performed. Although rumen fermentation predictions were not significantly improved, rumen volatile fatty acids absorption rates were recalculated, which improved the concordance correlation coefficient (CCC) for rumen propionate and ammonia concentration predictions but decreased CCC for acetate predictions. Similar degradation rates of crude protein were observed for grass and total mixed ration diets, but rumen-undegradable protein predictions seemed to be affected by the solubility of the protein source as was the intestinal digestibility coefficient. Ruminal fiber degradation was greater after reparameterization, driven primarily by hemicellulose degradation. Predictions of ruminal and fecal outflow of neutral detergent fiber and acid detergent fiber, as well as total fecal output predictions, improved significantly after reparameterization. Blood urea N and urinary N excretion predictions resulted in similar accuracy using both sets of model parameters, whereas fecal N excretion predictions were significantly improved after reparameterization. Body weight and body condition score predictions were greatly improved after model modifications and reparameterization. Before reparameterization, yield predictions for daily milk, milk fat, milk protein, and milk lactose were greatly overestimated (mean bias of 61.0, 58.7, 73.7, and 64.6% of mean squared error, respectively). Although this problem was partially addressed by model modifications and reparameterization (mean bias of 3.2, 1.1, 1.7, and 0.4% of mean squared error, respectively), CCC values were still small. The ability of the model to predict grass digestion and animal performance in dairy cows consuming pasture-based diets was improved, demonstrating the applicability of this model to these productive systems. However, the failure to predict grass digestion based on standard model inputs without reparameterization indicates there are still fundamental challenges in characterizing feeds for this model.
Collapse
Affiliation(s)
- A G Morales
- Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg 24061; Animal Science Institute, Universidad Austral de Chile, Valdivia 5110566, Chile
| | - R E Vibart
- AgResearch, Grasslands Research Centre, Tennent Drive, Palmerston North 4442, New Zealand
| | - M M Li
- Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg 24061
| | - A Jonker
- AgResearch, Grasslands Research Centre, Tennent Drive, Palmerston North 4442, New Zealand
| | - D Pacheco
- AgResearch, Grasslands Research Centre, Tennent Drive, Palmerston North 4442, New Zealand
| | - M D Hanigan
- Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg 24061.
| |
Collapse
|
5
|
Dong R, Sun G, Yu G. Estimating in vitro ruminal ammonia-N using multiple linear models and artificial neural networks based on the CNCPS nitrogenous fractions of cattle rations with low concentrate/roughage ratios. J Anim Physiol Anim Nutr (Berl) 2021; 106:841-853. [PMID: 34110053 DOI: 10.1111/jpn.13588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 04/17/2021] [Accepted: 05/14/2021] [Indexed: 11/27/2022]
Abstract
The objectives of this study were to investigate the relationship between the in vitro ruminal ammonia nitrogen (NH3 -N) concentration and the Cornell Net Carbohydrate and Protein System (CNCPS) N-fractions of feeds for cattle and further compare the performance of developing multiple linear regression (MLR) and artificial neural network (ANN) models in estimating the NH3 -N concentration in rumen fermentation. Two data sets were established, of which the training data set containing forty-five rations for cattle with concentrate/roughage ratios of 50:50, 40:60, 30:70, 20:80 and 10:90 used for developing models and the test data set containing ten other rations with the same concentrate/roughage ratios with the training data set were used for validating of models. The NH3 -N concentrations of feed samples were measured using an in vitro incubation technique. The CNCPS N-fractions (g), for example PB1 (rapidly degraded true protein), PB2 (neutral detergent soluble nitrogen), PB3 (acid detergent soluble nitrogen) of rations, were calculated based on chemical analysis. Statistical analysis indicated that the NH3 -N concentration (mg) was significantly correlated with the CNCPS N-fractions (g) PB1 , PB2 and PB3 in a multiple linear pattern: NH3 -N = (130.70±33.80) PB1 + (155.83±17.89) PB2 - (85.44±37.69) PB3 + (42.43±1.05), R2 = 0.77, p < 0.0001, n = 45. The results indicated that both MLR and ANN models were suitable for predicting in vitro NH3 -N concentration of rations using CNCPS N-fractions PB1 , PB2 , and PB3 as independent variables while the neural network model showed better performance in terms of greater r2 , CCC and lower RMSPE between the observed and predicted values.
Collapse
Affiliation(s)
- Ruilan Dong
- College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
| | - Guoqiang Sun
- College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
| | - Guanghui Yu
- College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
| |
Collapse
|
6
|
Li MM, Hanigan MD. A revised representation of ruminal pH and digestive reparameterization of the Molly cow model. J Dairy Sci 2020; 103:11285-11299. [PMID: 33041031 DOI: 10.3168/jds.2020-18372] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 08/02/2020] [Indexed: 12/18/2022]
Abstract
Ruminal pH is a critical factor to regulate nutrient degradation and fermentation. However, it has been poorly predicted in the Molly cow model, and recent improvements in the representation of nitrogen cycling across the rumen wall altered some of the modeled responses to feed nutrients, resulting in some model bias. The objectives of this study were to further improve the representation of pH and to refit parameters related to ruminal metabolism and nutrient digestion in the model to resolve this bias, and to use the improved model to estimate nitrogen and energy fluxes with varying rumen-degradable protein (RDP; 40 vs. 60%) and ruminally degraded starch (RDSt; 50 vs. 75%). A meta data set containing 284 peer reviewed studies with 1,223 treatment means was used to derive parameter estimates for ruminal metabolism and nutrient digestions. Refitting the parameters significantly improved the accuracy and precision of the model predictions for ruminal nutrient outflow [acid detergent fiber (ADF), neutral detergent fiber (NDF), total N, microbial N, nonammonia N, and nonammonia nonmicrobial N], ammonia and blood urea concentrations, and fecal nutrient outflow (protein, ADF, and NDF). The prediction error for body weight was decreased from 19.3 to 6.2% with decreased mean bias (from 76.0 to 11.5%) and slope bias (from 17.2 to 7.7%), primarily due to improved representations of ruminal dry matter and liquid pool size. Adding ammonia concentration as a driver to the pH equation increased the precision of predicted ruminal pH and, thereby, the precision of predicted volatile fatty acid (VFA) concentrations, due to improved representation of pH regulation of VFA production rates. Although minor mean and slope bias were observed for ruminal pH and VFA concentrations, the concordance correlation coefficients indicated that much of the observed variation in these variables remains unexplained. Overall, the biological functions of nutrient degradation and digestion appear to be represented without bias. Simulated results indicated that decreasing RDP and RDSt proportions in an isonitrogenous and isocaloric diet can slightly improve N efficiency, and increasing RDSt proportions can increase energy efficiency.
Collapse
Affiliation(s)
- Meng M Li
- Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg 24061
| | - Mark D Hanigan
- Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg 24061.
| |
Collapse
|
7
|
Hanigan MD, Daley VL. Use of Mechanistic Nutrition Models to Identify Sustainable Food Animal Production. Annu Rev Anim Biosci 2020; 8:355-376. [PMID: 31730368 DOI: 10.1146/annurev-animal-021419-083913] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
To feed people in the coming decades, an increase in sustainable animal food production is required. The efficiency of the global food production system is dependent on the knowledge and improvement of its submodels, such as food animal production. Scientists use statistical models to interpret their data, but models are also used to understand systems and to integrate their components. However, empirical models cannot explain systems. Mechanistic models yield insight into the mechanism and provide guidance regarding the exploration of the system. This review offers an overview of models, from simple empirical to more mechanistic models. We demonstrate their applications to amino acid transport, mass balance, whole-tissue metabolism, digestion and absorption, growth curves, lactation, and nutrient excretion. These mechanistic models need to be integrated into a full model using big data from sensors, which represents a new challenge. Soon, training in quantitative and computer science skills will be required to develop, test, and maintain advanced food system models.
Collapse
Affiliation(s)
- Mark D Hanigan
- Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA; ,
| | - Veridiana L Daley
- Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA; , .,National Animal Nutrition Program (NANP), Department of Animal & Food Sciences, University of Kentucky, Lexington, Kentucky 40546, USA
| |
Collapse
|
8
|
Affiliation(s)
- Douglas M Liebe
- Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA
| | - Robin R White
- Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA
| |
Collapse
|
9
|
Li MM, Sengupta S, Hanigan MD. Using artificial neural networks to predict pH, ammonia, and volatile fatty acid concentrations in the rumen. J Dairy Sci 2019; 102:8850-8861. [PMID: 31378500 DOI: 10.3168/jds.2018-15964] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 05/15/2019] [Indexed: 11/19/2022]
Abstract
The objectives of this study were (1) to predict ruminal pH and ruminal ammonia and volatile fatty acid (VFA) concentrations by developing artificial neural networks (ANN) using dietary nutrient compositions, dry matter intake, and body weight as input variables; and (2) to compare accuracy and precision of ANN model predictions with that of a multiple linear regression model (MLR). Data were collected from 229 published papers with 938 treatment means. The data set was randomly split into a training data set containing 70% of the observations and a test data set with the remaining observations. A series of ANN with a range of 1 to 9 artificial neurons in 1 hidden layer were examined, and the best one was selected to compare with the best-fitted MLR model. The performance of model predictions was evaluated by root mean square errors (RMSE) and concordance correlation coefficients (CCC) using cross-evaluations with 100 iterations. When using the ANN to predict ruminal pH and concentrations of ammonia, total VFA, acetate, propionate, and butyrate, the RMSE were 4.2, 41.4, 20.9, 22.3, 32.9, and 29.7% of observed means, respectively. The RMSE for the MLR were 4.2, 37.8, 18.3, 19.9, 29.8, and 26.6% of the observed means. The CCC for ruminal pH, ruminal concentrations of ammonia, total VFA, acetate, propionate, and butyrate were 0.57, 0.49, 0.45, 0.40, 0.52, and 0.40, using the ANN, and 0.37, 0.48, 0.40, 0.29, 0.43, and 0.35, using the MLR. Evaluations of the MLR and the ANN indicated that these 2 model forms exhibited similar prediction errors, with 4.2, 39.6, 19.6, 21.1, 31.3, and 28.1% of observed means for pH, ammonia, total VFA, acetate, propionate, and butyrate. Although the ANN increased the precision of predictions related to ruminal metabolism, it failed to improve the accuracy compared with the linear regression model.
Collapse
Affiliation(s)
- Meng M Li
- Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg 24061
| | - Srijan Sengupta
- Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg 24061
| | - Mark D Hanigan
- Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg 24061.
| |
Collapse
|
10
|
Li MM, Titgemeyer EC, Hanigan MD. A revised representation of urea and ammonia nitrogen recycling and use in the Molly cow model. J Dairy Sci 2019; 102:5109-5129. [PMID: 30904308 DOI: 10.3168/jds.2018-15947] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 01/27/2019] [Indexed: 01/21/2023]
Abstract
Accurately predicting nitrogen (N) digestion, absorption, and metabolism will allow formulation of diets that more closely match true animal needs from a broad range of feeds, thereby allowing efficiency of N utilization and profit to be maximized. The objectives of this study were to advance representations of N recycling between blood and the gut and urinary N excretion in the Molly cow model. The current work includes enhancements (1) representing ammonia passage to the small intestine; (2) deriving parameters defining urea synthesis and ruminal urea entry rates; (3) adding representations of intestinal urea entry, microbial protein synthesis in the hindgut, and fecal urea-N excretion; and (4) altering existing urinary N excretion equations to scale with body weight and adding purine derivatives as a component of urinary N excretion. After the modifications, prediction errors for ruminal outflows of total N, microbial N, and nonammonia, nonmicrobial N were 29.8, 32.3, and 26.2% of the respective observed mean values. Prediction errors of each were approximately 7 percentage units lower than the corresponding values before model modifications and fitting due primarily to decreased slope bias. The revised model predicted ruminal ammonia and blood urea concentrations with substantially decreased overall error and reductions in slope and mean bias. Prediction errors for gut urea-N entry were decreased from 70.5 to 26.7%, which was also a substantial improvement. Adding purine derivatives to urinary N predictions improved the accuracy of predictions of urinary N output. However, urinary urea-N excretion remains poorly predicted with 69.0% prediction errors, due mostly to overestimated urea-N entry rates. Adding representations of undigested microbial nucleic acids, microbial protein synthesized in the hindgut, and urea-N excretion in feces decreased prediction errors for fecal N excretion from 21.1 to 17.1%. The revised model predicts that urea-N entry into blood accounts for approximately 64% of dietary N intake, of which 64% is recycled to the gut lumen. Between 48 and 67% of the urea recycled to the gut flows into the rumen largely depending on diet, which accounts for 29 to 54% of total ruminal ammonia production, and 65 to 76% of this ammonia-N is captured in microbial protein, which represents 17% of N intake. Based on model simulations, feeding a diet with moderately low crude protein and high rumen-undegradable protein could increase apparent ruminal N efficiency by 20%.
Collapse
Affiliation(s)
- Meng M Li
- Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg 24061
| | - E C Titgemeyer
- Department of Animal Sciences and Industry, Kansas State University, Manhattan 66506-1600
| | - Mark D Hanigan
- Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg 24061.
| |
Collapse
|