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

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

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This dissertation aims to develop and evaluate prediction models for ruminal pH in beef cattle, focusing on the impact of diet composition and ruminal fermentation characteristics. It explores the use of existing models and the development of new models based on various data sources and measurement techniques. The work contributes to a better understanding of ruminal pH dynamics in beef cattle and provides valuable insights for improving nutritional management strategies to optimize animal health and productivity.
• Predicting ruminal pH in beef cattle using mathematical models
• Evaluating the accuracy and performance of existing ruminal pH prediction models
• Developing new prediction models based on dietary compositions and ruminal fermentation characteristics
• Understanding the influence of diet composition and ruminal fermentation on ruminal pH
• Providing insights for optimizing nutritional management practices for beef cattle health and productivity

Zusammenfassung der Kapitel (Chapter Summaries)

• Chapter 1: Introduction - This chapter provides an overview of ruminal acidosis in beef cattle, highlighting its significance and the need for effective prediction models. It discusses the use of mathematical models in animal nutrition, particularly for predicting ruminal pH, and reviews existing models based on dietary compositions and fermentation end-products.
• Chapter 2: Materials and Methods - This chapter details the methodology used in the dissertation, outlining the database compilation, description, dietary compositions, and ruminal fermentation characteristics. It further describes the process of developing new prediction equations, evaluating model adequacy, and performing residual analysis.
• Chapter 3: Results - This chapter presents the results of the study, covering descriptive statistics of the literature data, correlation analyses, and the development and evaluation of ruminal pH prediction models using various measurement techniques.
• Chapter 4: Discussion - This chapter analyzes and interprets the results, examining the performance of existing models, the use of ruminal fermentation characteristics and dietary composition in pH prediction, and the recommended equations for predicting mean ruminal pH. It also suggests potential areas for future research.

Schlüsselwörter (Keywords)

This work focuses on the crucial areas of animal nutrition, sustainable agriculture, and beef cattle health. Key terms include ruminal pH, prediction models, dietary composition, ruminal fermentation, and acidosis in beef cattle. The study utilizes a comprehensive database, allowing for the development and evaluation of accurate models for predicting ruminal pH in beef cattle, contributing to improved animal health and productivity.

Frequently Asked Questions

Why is predicting ruminal pH important in beef cattle production?

Predicting ruminal pH is crucial to avoid ruminal acidosis, a condition that affects animal health and productivity. Accurate prediction models allow for monitoring without using invasive techniques like rumen cannulas.

What are the key predictors used for modeling ruminal pH?

The main predictors include dietary variables such as physically effective fiber (peNDF) and ruminal fermentation end-products like volatile fatty acids (VFAs).

What is physically effective fiber (peNDF)?

peNDF refers to the fraction of fiber in the diet that stimulates chewing activity and helps maintain a stable ruminal environment through saliva production.

Which existing models were evaluated in this research?

The study tested models by Mertens (1997), Pitt et al. (1996), Fox et al. (2004), and Zebeli et al. (2006/2008), among others.

How was the database for this study compiled?

The database included treatment means from 65 peer-reviewed publications spanning from 1969 to 2014, covering animal characteristics, ration composition, and fermentation data.

What is the originality of this modeling approach?

The research provides effective coefficients specifically adapted to beef cattle production systems (backgrounding, finishing, zero-grazing) for the first time.