A Framework for Integrating Gut Microbiota into ruminant Feed Efficiency Models

Thesis Abstract

The study addresses the persistent gap between conventional feed efficiency models for ruminants and the dynamic role of the gut microbiota in modulating nutrient utilization, methane emission, and host energy balance. Despite advances in host genetics and diet formulation, current models inadequately capture microbiome-driven variation in feed conversion efficiency, leading to suboptimal predictions and limited precision in management decisions. The aim is to develop a framework integrating gut microbiota signatures with phenotypic and environmental predictors to improve feed efficiency modeling in ruminants. Specific objectives are (i) to characterize rumen and lower-gut microbiota composition and function in a representative cohort; (ii) to identify microbial taxa and functional pathways consistently associated with residual feed intake and metabolic efficiency under varied diets; (iii) to formulate a multi-level analytical framework combining microbiome-derived features with host phenotypes using hierarchical regression and network-based approaches; (iv) to validate the framework with independent datasets and assess its predictive performance against conventional models; and (v) to propose practical recommendations for incorporating microbiome data into decision-support tools for nutritionists. A mixed-methods design will be employed. The population will comprise 320 beef cattle and 260 dairy cattle sourced from commercial feedlots and research herds across three regions, representing diverse feeding regimens. Urine and blood biomarkers, feed intake, body weight gain, and methane yield will be collected over a 12-week period. Gut microbial communities will be profiled using 16S rRNA gene sequencing and shotgun metagenomics on rumen fluid and fecal samples collected at weeks 0, 6, and 12. Diet composition, housing conditions, and health status will be recorded to control for confounders. Data collection instruments include validated feed intake logs, automated weighing systems, gas capture chambers for methane measurement, and high-throughput sequencing platforms (Illumina NovaSeq) with downstream bioinformatics pipelines (QIIME2 for amplicon data; HUMAnN3 for functional profiling). Validity and reliability will be addressed through calibration of feed intake sensors, replicate sampling, and cross-validation of sequencing runs. Data analysis will proceed in three stages. First, descriptive statistics and feature reduction will identify core microbial taxa and functional pathways linked to feed efficiency metrics. Second, hierarchical linear models and Bayesian networks will quantify associations between microbiota features and feed efficiency while accounting for fixed effects (diet, breed, age) and random effects (pen, farm). Third, predictive models integrating microbial features with host phenotypes will be compared against baseline models using R-squared, RMSE, and AUC where appropriate. Model specification will include interaction terms (microbiota-diet) and non-linear effects assessed via generalized additive models. Causality considerations will be guided by Bradford Hill criteria and supported by sensitivity analyses. Network analysis will explore microbe–metabolite–host phenotype interactions, highlighting key keystone taxa and pathways (e.g., methanogenesis, fiber degradation, volatile fatty acid production) that underpin efficiency. Key expected findings include identification of a core microbiome signature predictive of improved feed efficiency, robust associations between specific microbial pathways (e.g., fiber degradation enzymes, acetogenesis) and host energy harvest, and enhanced predictive accuracy when microbiome features are integrated into models (anticipated 15–25% improvement in RMSE reduction over standard models). The study will contribute to knowledge by operationalizing a transferable framework that links microbial ecology to host nutrition models, enabling more accurate simulations of feed performance under real-world conditions. It is anticipated that the framework will reveal interactions between diet composition and microbial communities that modulate efficiency and methane output, informing targeted dietary interventions and microbiome-informed breeding strategies. The main conclusion is that incorporating gut microbiota into ruminant feed efficiency models substantially improves predictive power and provides mechanistic insight into nutrient utilization. Recommendations include developing standardized microbiome sampling protocols for routine farm use, incorporating microbial features into commercial prediction tools, and conducting longitudinal validation across production systems to refine the framework for broader applicability.