Systematic review for optimizing sample size in dairy cow methane emission studies in temperate regions: a comprehensive methodological approach.

ABSTRACT

This research introduces a systematic framework for calculating sample size in studies focusing on enteric methane (CH4, g/kg of DMI) yield reduction in dairy cows. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search across the Web of Science, Scopus, and PubMed Central databases for studies published from 2012 to 2023. The inclusion criteria were studies reporting CH4 yield and its variability in dairy cows, employing specific experimental designs (Latin Square Design (LSD), Crossover Design, Randomized Complete Block Design (RCBD), and Repeated Measures Design) and measurement methods (Open-circuit respirometry chambers (RC), the GreenFeed system, and the sulfur hexafluoride tracer technique), conducted in Canada, the United States and Europe. A total of 150 studies, which included 177 reports, met our criteria and were included in the database. Our methodology for using the database for sample size calculations began by defining 6 CH4 yield reduction levels (5, 10, 15, 20, 30, and 50%). Utilizing an adjusted Cohen's f formula and a power analysis we calculated the sample sizes required for these reductions in balanced LSD and RCBD reports from studies involving 3 or 4 treatments. The results indicate that within-subject studies (i.e., LSD) require smaller sample sizes to detect CH4 yield reductions compared with between-subject studies (i.e., RCBD). Although experiments using RC typically require fewer individuals due to their higher accuracy, our results demonstrate that this expected advantage is not evident in reports from RCBD studies with 4 treatments. A key innovation of this research is the development of a web-based tool that simplifies the process of sample size calculation (samplesizecalculator.ucdavis.edu). Developed using Python, this tool leverages the extensive database to provide tailored sample size recommendations for specific experimental scenarios. It ensures that experiments are adequately powered to detect meaningful differences in CH4 emissions, thereby contributing to the scientific rigor of studies in this critical area of environmental and agricultural research. With its user-friendly interface and robust backend calculations, this tool represents a significant advancement in the methodology for planning and executing CH4 emission studies in dairy cows, aligning with global efforts toward sustainable agricultural practices and environmental conservation.