Prediction of Ruminal pH for Beef Cattle. A Physiological Modelling Approach

Diploma Thesis , 2015 , 175 Pages , Grade: 108/110

Agrarian Studies

Ruminal fluid pH (RpH) is an important parameter for controlling the rumen functions. The ability to predict the RpH of beef cattle fed a given diet without depending on the invasive techniques for its measurements (i.e., rumen cannula) is important to avoid ruminal acidosis. The objectives of this research were to: (i) identify key variables that have a significant associations with RpH; (ii) collect data points (DB) from in-vivo beef cattle studies to identify suitable predictors of RpH after considering the animal measures and the dietary variables from a wide range of diets that can safely be fed to beef cattle; (iii) evaluate the extant RpH models relevant to the study; and (iv) develop a new statistical models for mean RpH predictions. Therefore, feed additives (i.e., monensin) were excluded from the analysis. Models tested that use physically effective fiber (peNDF) as a dependent variable were Pitt et al. (1996, PIT), Mertens (1997, MER), Fox et al. (2004, FOX), Zebeli et al. (2006, ZB6), and Zebeli et al. (2008, ZB8), and those that use rumen volatile fatty acids (VFAs) were Tamminga and Van Vuuren (1988, TAM), Lescoat and Sauvant (1995, LES), and Allen (1997, ALL). The final database was categorized into DB (1) and (2) that included a total of 232 and 95 treatment means from 65 and 26 peer-reviewed publications, respectively, spanning from the 1969s to 2014. The DB included information on animal characteristics, ration composition, and ruminal fermentation and pH, that has been used for independent evaluation and development of RpH prediction models. The average bodyweight was 437±168 vs.556±114 kg, dry matter intake (DMI) was 8.57±2.62 vs.9.60±2.10 kgd-1, peNDF (% DM) was 20.3±17.0 vs.17.2±14.6, and forage (% DM) was 34.8±36.1 vs.26.9±31.0 for DB (1) and (2), respectively. The cattle used were of various ages (i.e., calves, yearlings, mature) and represented various production systems (i.e., backgrounding, finishing, and zero-grazing). The originality of our work is to provide for the first time effective coefficients that are better adapted to beef cattle production. The external validation remains to be done to confirm the effect of the integration of environmental, nutritional, and microbial factors on the RpH fluctuations, using a resilient data source, because of their vitality in accurately predicting the animal responses.

Inhaltsverzeichnis (Table of Contents)

• Abstract
• Résumé
• Riassunto
• Abbreviations and acronyms
• 1. Introduction
• 1.1 Changes in the Mean Ruminal pH Profile of Beef Cattle during Acidosis
• 1.2 Mathematical Modelling in Animal Nutrition
• 1.2.1 Prediction of the Mean Ruminal pH from Dietary Compositions
• 1.2.1.1 Mertens, (1986-1997)
• 1.2.1.2 Cornell Net Carbohydrate Protein System, (1992-2008)
• 1.2.1.3 Zebeli et al. (2006) and (2008) models
• 1.2.2 Prediction of the Mean Ruminal pH from Ruminal Fermentation end-products
• 1.2.2.1 Tamminga and Van Vuuren, (1988)
• 1.2.2.2 Institut National de la Recherche Agronomique, (1995)
• 1.2.2.3 Allen, (1997)
• 2. Materials and Methods
• 2.1 Database Compilation
• 2.2 Database Description
• 2.3 Dietary Compositions and Missing Values
• 2.4 Ruminal Fermentation Characteristics and Calculations
• 2.5 Extant Prediction Equations
• 2.6 Development of new prediction equations
• 2.7 Models adequacy and evaluation
• 2.8 Residual analysis
• 3. Results
• 3.1. Descriptive Statistics of Literature Data
• 3.2. Correlation Analyses of Literature Data
• 3.3. Development of mean Rumen pH prediction models from all of the pH measurements observations
• 3.4. Development of mean Rumen pH prediction models from continuously measured observations
• 3.5. Evaluation of extant Rumen pH prediction models
• 3.5.1 Performance of the tested models against all the different rumen pH measurements.observations
• 3.5.2 Performance of the tested models against continuously measured rumen pH observations
• 4. Discussion
• 4.1. Ruminal pH Prediction from the extant published models
• 4.2 Use of ruminal fermentation characteristics (VFA) in mean Ruminal pH Prediction
• 4.3 Use of dietary composition and ruminal variables in mean Ruminal pH Prediction
• 4.4 Recommended Equations for prediction of mean Rumen pH for beef cattle
• 4.5 Recommendations for Further Research
• 5. Conclusion
• 6. Appendix
• 6.1. List of Tables
• Table 1. Comparison of acute and sub-acute acidosis in beef cattle
• Table 2. Main Factors affecting ruminal acidosis in beef cattle
• Table 3. Factors affecting ruminal pH in beef cattle
• Table 4. Descriptive statistics of database (1)
• Table 5. Descriptive statistics of database (2)
• Table 6. Summary of database (1) used to evaluate the performance of the published ruminal pH prediction models and for the modulation of new ruminal pH prediction equations from the different published ruminal pH measurement techniques
• Table 7. Summary of database (2) used to evaluate the performance of the published ruminal pH prediction models and for the modulation of new ruminal pH prediction equations from the different published ruminal pH measurement techniques
• Table 8. Publications included in the database used in modeling the ruminal pH from in-vivo beef cattle measurements
• Table 9. Description of dataset assembled from different studies categorized into: Author, location of study, type of study, main grain type in the diet, and diet type
• Table 10. Description of dataset assembled from different studies categorized into: main ingredients in diets, initial bodyweight and average body weight
• Table 11. Description of dataset assembled from different studies categorized into: treatment description and diets compositions (Forage, CP, NDF, peNDF, and predicted peNDF from Pitt et al. (1996), Mertenes (1996), Zebeli et al. (2008), and Fox et al. (2004) equations
• Table 12. Description of dataset assembled from different studies categorized into: the animal pH sampling method; post-feeding times; DMI; and dietary compositions of the diets (ADF, NDF, Forage NDF, Lignin, NFC, Sugar, Starch, SS, EE, and Ash)
• Table 13. Description of the dataset assembled from different studies categorized into: predicted RpH from (Lescoat and Sauvant, 1995, LES; Pitt et al., 1996, PIT; Tamminga and Van Vuuren, 1988, TAM; Fox et al., 2004, FOX; and Zebeli et al., 2008, ZB8; observed mean RpH, minimum (nadir) RpH; time RpH < 5.2 (h); time RpH < 5.5 (h); time RpH < 5.6 (h); time RpH < 5.8 (h); total VFA, tVFA (mM); molar percentage of Acetate, AC; Propionate, PR; Butyrate, BU; and Ammonia, Am (mM) concentration
• Table 14. Description of the dataset assembled from different studies categorized into: dietary intake (% kg d¹) of Forage, DM, CP, ADF, peNDF, peNDF from Pitt et al. (1996), Fox et al. (2004) and Mertenes (1996) equations, NDF, Forage NDF, Lignin, Sugar, Starch, SS, EE and Ash
• Table 15.1. Across-study linear relationship for the animal response to ruminal pH from database (1) variables
• Table 15.2. Across-study quadratic relationship for the animal response to ruminal pH from database (1) variables

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This dissertation aims to develop and evaluate models for predicting ruminal pH in beef cattle using a physiological modelling approach. The research focuses on utilizing dietary compositions and ruminal fermentation characteristics to improve the accuracy of pH predictions.

• The impact of dietary composition on ruminal pH in beef cattle
• The role of ruminal fermentation characteristics in predicting ruminal pH
• Development and evaluation of new prediction models for ruminal pH
• Comparison of the performance of existing and newly developed models
• Recommendations for further research in the field of ruminal pH prediction

Zusammenfassung der Kapitel (Chapter Summaries)

Chapter 1 provides a comprehensive overview of ruminal pH and its importance in beef cattle nutrition. It discusses the changes in ruminal pH profiles during acidosis and explores the use of mathematical modelling in animal nutrition. This chapter reviews existing models for predicting ruminal pH based on dietary compositions and ruminal fermentation end-products.

Chapter 2 details the materials and methods used in this dissertation. It describes the compilation and description of the database used for model development and evaluation. The chapter outlines the dietary compositions, ruminal fermentation characteristics, and the extant prediction equations used in the study. It also explains the development of new prediction equations, model adequacy evaluation, and residual analysis.

Chapter 3 presents the results of the study. It includes descriptive statistics of the literature data, correlation analyses, and the development of mean ruminal pH prediction models. The chapter evaluates the performance of existing models against different ruminal pH measurement techniques and discusses the results of the model development process.

Chapter 4 provides a detailed discussion of the findings. It analyzes the performance of the extant published models for predicting ruminal pH and explores the use of ruminal fermentation characteristics in pH prediction. The chapter examines the use of dietary composition and ruminal variables in predicting mean ruminal pH and recommends equations for predicting mean ruminal pH in beef cattle. It concludes with recommendations for further research in the field.

Schlüsselwörter (Keywords)

The keywords and focus themes of this text include ruminal pH, beef cattle, acidosis, dietary composition, ruminal fermentation, mathematical modelling, prediction models, and animal nutrition. The dissertation explores the development and evaluation of models for predicting ruminal pH in beef cattle, utilizing dietary compositions and ruminal fermentation characteristics to improve the accuracy of predictions. The research aims to contribute to a better understanding of ruminal pH dynamics and its implications for beef cattle health and productivity.

• College: Université Paul-Valéry Montpellier 3 (Institut National de la Recherche Agronomique)
• Course: Management of Animal Resources and Sustainable Development in Agriculture
• Grade: 108/110
• Author: M.Sc. Management of Animal Resources and Sustainable Development in Agriculture Mohamed Sarhan (Author)
• Publication Year: 2015
• Pages: 175
• Catalog Number: V293688
• ISBN (eBook): 9783656913344
• ISBN (Book): 9783656913351
• Language: English
• Tags: Beef cattle, Modelling, Meta-Analysis, Prediction, pH, Rumen, Ruminal acidosis