ENTERIC METHANE EMISSIONS IN DAIRY COWS: MATERNAL NUTRITIONAL EFFECTS, PREDICTION MODELS, AND FARM-LEVEL STRATEGIES
Date
2025-10-20Author
Wu, Zhuonan
Department
Animal Sciences
Advisor(s)
Weigel, Kent
Metadata
Show full item recordAbstract
This thesis addresses two complementary strategies to support enteric methane mitigation in dairy cattle: maternal nutritional programming and milk mid-infrared (MIR) spectral prediction. Chapter One provides the biological and practical context, highlighting the environmental impact of methane, the challenges of on-farm measurement, and the potential of maternal nutrition and MIR spectra as scalable tools for mitigation. The chapter identifies key knowledge gaps, particularly regarding the long-term effects of maternal nutrition on offspring methane traits and the underutilized potential of MIR data. Chapter Two presents a longitudinal study evaluating whether maternal energy intake during late lactation affects the growth, feed efficiency, and methane emissions of offspring through their first lactation. Using a controlled feeding trial and the GreenFeed system to measure methane production at specific time points, the study found no significant effects of maternal diet on offspring performance. However, methane emissions increased with age and became more stable over time. Chapter Three focuses on the development and evaluation of statistical models to predict methane production using MIR milk spectra. Using data from 14 trials and multiple modeling strategies—including Partial Least Squares, BayesB, and kernel regression—the chapter compares prediction performance under various cross-validation schemes. Results showed that incorporating MIR spectra improved prediction accuracy compared to models using only fixed effects, with Bayesian and kernel methods outperforming PLS in most settings. These findings highlight the utility of MIR-based modeling for scalable methane monitoring. Chapter Four summarizes the major findings of the thesis and outlines future research directions in maternal nutrition, MIR modeling, genomic selection, and the identification of early-life biomarkers to advance methane mitigation strategies in dairy production systems.
Subject
Animal Sciences
Permanent Link
http://digital.library.wisc.edu/1793/96289Type
Thesis

