Methodological Considerations of High Frequency Automated Soil Respiration Measurements

Understanding the mechanisms regulating the efflux of carbon dioxide from the soil to the atmosphere via soil respiration (SR) is a critical component of studies of responses of the terrestrial carbon cycle to climate change and to management. High-quality measurements of SR fluxes are needed. Chamber artifacts and efforts to avoid them have been widely reviewed previously. Here, we focus on temporal considerations in light of recently available options for measurement automation. When developing a sampling strategy for SR measurements, researchers must consider the ultimate use of the dataset and whether the temporal scale of the research question requires data on diel, synoptic scale, seasonal, annual, or interannual variation. If the main objective is an annual estimate of CO2 efflux, then a weekly or biweekly manual sampling strategy is likely sufficient, and manual measurements also facilitate greater assessment of spatial heterogeneity. However, if diel variation and responses to synoptic scale weather patterns are important, then automated SR measurements are highly advantageous. Automated SR systems produce large volumes of data that present new challenges for quality assurance and quality control. A relatively efficient protocol to analyze large SR datasets is proposed here. Analysis of two large datasets provides information about systematic sampling uncertainties as well as random measurement errors and their applications to models.