Statistical Challenges in Analyses of Chamber‐Based Soil CO2 and N2O Emissions Data. Issue 1 (13th January 2015)

• Record Type: Journal Article
• Authors: Kravchenko, A. N.; Robertson, G. P.
• Is Part Of: Soil Science Society of America Journal. Volume 79:Issue 1(2015)
• Page Start: 200
• Page End: 211
• Publication Date: 2015-01-13
• Subjects: Soils -- United States -- Periodicals; Soil science -- Periodicals; Periodicals; 631.4973
• DOI: 10.2136/sssaj2014.08.0325
• Languages: English
• ISSNs: 0361-5995

Abstract

Abstract : 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 N2 O collected from three experimental sites in Michigan demonstrated high spatial and temporal variability for CO2 and N2 O 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 N2 O 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.