Emissions of CO2 from soils tend to exhibit predictable diurnal patterns correlated with environmental conditions, like temperature. Characterization of these underlying diurnal patterns is key to drawing meaningful conclusions from studies that examine the effects of treatment applications and agricultural practices on soil GHG emissions. Relatively little is known about drivers of N2O emissions, which are often temporally variable and do not typically display predictable diurnal patterns. Therefore, daily or weekly sampling approaches are insufficient to accurately estimate daily average emissions of N2O.
This case study uses data from an experimental farm to illustrate how continuous monitoring via an automated chamber system enables comprehensive characterization of diurnal CO2 emissions patterns, allowing for more robust comparisons between treatments. It also illustrates the temporal variability and range of N2O emissions, highlighting the importance of continuous data for estimation of average daily N2O emissions.
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