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.
摘要：
这项研究引入了一个系统的框架，用于计算研究中的样本量，重点是奶牛的肠甲烷（CH4，g/kg的MI）产量降低。遵循系统审查和荟萃分析（PRISMA）指南的首选报告项目，我们在科学网进行了全面的搜索，Scopus,和PubMedCentral数据库，用于2012年至2023年发表的研究。纳入标准是：报告奶牛CH4产量及其变异性的研究，采用特定的实验设计(拉丁广场设计(LSD)，交叉设计,随机完全区组设计（RCBD），和重复测量设计)和测量方法(开路呼吸测量室(RC)，GreenFeed系统,和六氟化硫示踪技术)，在加拿大进行，美国和欧洲。共150项研究，其中包括177份报告，符合我们的标准并被纳入数据库.我们使用数据库进行样本量计算的方法始于定义6个CH4产量降低水平（5、10、15、20、30和50％）。利用调整后的Cohen的f公式和功效分析，我们计算了从涉及3或4种治疗的研究中减少平衡LSD和RCBD报告所需的样本量。结果表明，受试者内研究(即，与受试者间研究相比，LSD)需要较小的样本量来检测CH4产量降低(即，RCBD)。尽管使用RC的实验通常需要较少的个体，因为它们具有较高的准确性，我们的结果表明,在4种治疗方法的RCBD研究报告中,这一预期优势并不明显.这项研究的一项关键创新是开发了一种基于Web的工具，该工具简化了样本量计算的过程（samplesizecalculator。ucdavis.edu）。使用Python开发，该工具利用广泛的数据库为特定的实验场景提供量身定制的样本量建议。它确保实验有足够的动力来检测CH4排放的有意义的差异，从而有助于科学严谨的研究在这个关键领域的环境和农业研究。凭借其用户友好的界面和强大的后端计算，该工具代表了在乳牛中计划和执行CH4排放研究的方法方面的重大进展，与全球可持续农业实践和环境保护努力保持一致。
