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ARS Home » Plains Area » Bushland, Texas » Conservation and Production Research Laboratory » Livestock Nutrient Management Research » Research » Publications at this Location » Publication #418091
Research Project: Strategies to Manage Feed Nutrients, Reduce Gas Emissions, and Promote Soil Health for Beef and Dairy Cattle Production Systems of the Southern Great Plains

Location: Livestock Nutrient Management Research

Title: Impact of methane mitigation strategies on the native ruminant microbiome: A protocol for a systematic review and meta-analysis

Author
item Frazier, Anthony - Nathan
item BELK, AERIEL - Auburn University
item Beck, Matthew
item Koziel, Jacek

Submitted to: OSF Preprints
Publication Type: Other
Publication Acceptance Date: 8/12/2024
Publication Date: 8/12/2024
Citation: Frazier, A.N., Belk, A.D., Beck, M.R., Koziel, J.A. 2024. Impact of methane mitigation strategies on the native ruminant microbiome: A protocol for a systematic review and meta-analysis. OSF Preprints/Registries. https://doi.org/10.17605/OSF.IO/VT56C.
DOI: https://doi.org/10.17605/OSF.IO/VT56C

Interpretive Summary:

Technical Abstract: Methane (CH4) is a potent greenhouse gas (GHG) that contributes to climate warming. Agriculture, primarily from the cattle industry, is the largest sector for anthropogenic CH4 emissions. Recently, research has begun investigating the role of the ruminant native microbiome and the role microbes play in CH4 production and mitigation. However, the variation across microbiome studies makes implementing impactful strategies difficult due to the different methodologies within microbial ecology research. The first objective of this study is to identify, summarize, compile, and discuss the current literature on CH4 mitigation strategies and how they interact with the native ruminant microbiome. The second objective is to perform a meta-analysis on the identified and compiled 16S rRNA sequencing data. A literature search using Web of Science, Scopus, AGRIS (International Information Systems for Agricultural Sciences and Technology), and Google Scholar will be implemented. Eligible criteria will be defined using PICO elements. Two independent reviewers will be utilized for both the literature search and data compilation. Risk of bias will be assessed using the Cochrane Risk Bias 2.0 tool. Publicly available 16S rRNA amplicon gene sequencing data will be downloaded from NCBI Sequence Read Archive using appropriate extraction methods. Data processing will be performed using QIIME2 following a standardized protocol. Meta-analyses will be performed on both alpha and beta diversity as well as taxonomic analyses. Alpha diversity will be measured using three metrics: Shannon’s Diversity Index, Faith’s Phylogenetic Diversity, and richness. For beta diversity, both weighted and unweighted UniFrac distance will be analyzed and statistically tested using PERMANOVA testing with multiple test corrections. Hedge’s g standardized mean difference statistic will be used to calculate fixed and random effects model estimates using a 95% confidence interval. Heterogeneity between studies will be assessed using the I2 statistic. Potential publication bias will be further assessed using Begg’s correlation test and Egger’s regression test. Potential bias will be indicated if the number of studies is greater than 10 with a P-value < 0.1. The GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach will be used to assess the certainty of evidence. The following protocol will be used to guide future research and meta-analyses for investigating CH4 mitigation strategies and ruminant microbial ecology. The future work could be used to enhance livestock management techniques for GHG control. This protocol is registered in Open Science Framework (link available after peer review process) and available in the Systematic Reviews for Animals and Food (https://www.syreaf.org/contact).