Statistical Challenges in Analyses of Chamber-Based Soil CO2 and N2O Emissions Data
Statistical Challenges in Analyses of Chamber-Based Soil CO2 and N2O Emissions Data
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DOI：
10.2136/sssaj2014.08.0325
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发表时间：
2015-01-01
影响因子：
2.9
通讯作者：
Robertson, G. P.
中科院分区：
文献类型：
--
作者：
Kravchenko, A. N.;Robertson, G. P.
土壤温室气体（GHG）排放量的测量已经获得了很多关注，以努力增加农业在缓解气候影响方面的作用。然而，似乎没有很好地认识到，基于室的温室气体数据的性质是这样的，分析需要先进的统计技术，以充分探索实验处理效果。此外，对于土壤温室气体数据，一些实验设计方法可以增强，而其他方法可以削弱研究检测处理差异的能力。在这里，我们确定和探索的实验设计和统计分析相关的室内土壤温室气体研究的关键选择的影响。特别是，我们讨论了（一）温室气体实地研究中随机变化的不同来源的相对贡献，（二）在不同的复制水平增加样本数量，以增加统计能力的相对好处，（三）会计异质性方差和使用重复测量分析温室气体研究的好处。从密歇根州的三个实验地点收集的CO2和N2O排放数据表明，CO2和N2O通量的空间和时间变异性很高。对于这两种气体的总变率是占主导地位的小尺度时空变率源，其中占55%的CO2和95%的N2O通量的总变率。虽然增加重复图的数量是提高统计功效的主要途径，但增加每个重复图的子样本（室和气体样本）数量也可以提供实质性收益。明智的重复测量分析，特别是考虑异质性方差是有效分析基于温室气体数据的重要策略。
Measurements of soil greenhouse gas (GHG) emissions have gained a lot of attention in an effort to potentially increase agriculture's role in mitigating climate effects. However, it seems not well recognized that the nature of chamber- based GHG data is such that analyses require advanced statistical techniques to fully explore experimental treatment effects. Moreover, for soil GHG data some experimental design approaches can enhance while others can weaken a study's ability to detect treatment differences. Here we identify and explore the implications of key choices in experimental design and statistical analyses relevant to chamber-based soil GHG studies. In particular, we discuss (i) relative contributions of different sources of random variability in GHG field studies, (ii) relative benefits of increasing the numbers of samples at different replication levels to increase statistical power, and (iii) benefits of accounting for heterogeneous variances and using repeated measures analysis in GHG studies. Emissions data for CO2 and N2O collected from three experimental sites in Michigan demonstrated high spatial and temporal variability for CO2 and N2O fluxes. For both gases the total variability is dominated by small-scale spatiotemporal variability sources, which constituted 55% of the total variability for CO2 and 95% for N2O fluxes. While increasing the number of replicate plots is the main route of rising statistical power, increasing the number of subsamples (chambers and gas samples) per replicate plot can also provide substantial gains. Judicious repeated measures analysis and especially accounting for heterogeneous variances are important strategies for the efficient analysis of chamber-based GHG data.