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.

Study Objectives and Equipment

Agriculture accounts for ~10% of total global GHG emissions, and ~70% of N2O emissions — a GHG with a warming potential 300 times greater than CO2. In order to mitigate the impacts of climate change, it is essential to better understand these emissions and develop reduction strategies while maintaining crop yield levels. 

Understanding the impact of agricultural practices on soil GHG emissions motivated Dr. Saikawa and her team to conduct this study. Emissions from plots of corn were measured using both manual chambers and Eosense eosAC automated soil gas flux chambers, an eosMX multiplexer and a Picarro G2508 gas analyzer. Dr Saikawa chose an automated chamber system in order to capture diurnal patterns in CO2 emissions and to better understand daily average N2O emissions, both of which are difficult to characterize using their current weekly sampling program and manual soil gas flux chambers. 

The Study Site

Figure 1. This study was conducted east of Atlanta, Georgia at the Iron Horse Plant Sciences Farm. The location of the farm is indicated by the red marker on the map. The farm is owned by the University of Georgia and used for agricultural research and experimentation.

The study site is located east of Atlanta on the Iron Horse Plant Sciences Farm (Figure 1). The 650 acre farm is owned by the University of Georgia and is used for agricultural research. The climate is humid, subtropical with temperatures typically varying between 2°C and 32°C and annual precipitation of ~1,220 mm. The study site consists of 36 plots located in a no-till corn field with 9 distinct combinations of agricultural practice and treatment applications, with 4 replicates of each combination. Agricultural practices include conventional (i.e. no cover cropping), cereal rye cover crop, and soybean intercrop. Three pesticide treatment levels (none, low and high) were also applied to plots of each agricultural practice. Sampling was conducted during the summer months (June, July and August).

Collecting Measurements

Dr. Saikawa and her team collected measurements from no and high treatment plots using both manual and automated eosAC chambers on all three agricultural practices (6 plots in total). Six eosAC automated soil gas flux chambers were deployed in the corn field, with a single chamber installed in each of the plot types. The collars for both the eosAC and manual chambers were installed and left in place for the duration of the study (Figure 3). The eosAC chambers were deployed and allowed to measure continuously for 24-36 hours on a weekly basis. The measurement cycle consisted of a 10 minute closure and all gas lines were purged for 2 minutes before and after each measurement. Manual chamber sampling of all 36 plots was conducted on a weekly basis, taking a total of ~12 hours to collect a single flux measurement from each plot. For this case study, we will focus on only the results from the conventional plots with none and high treatment levels.

Figure 2. The eosMX multiplexer and Picarro G2508 gas analyzer deployed at the field site
Figure 3. Co-located sampling locations for the eosAC automated soil gas flux chamber and the manual chamber system.

Findings Thus Far

A box plot comparison of CO2 emissions between conventional agricultural plots with and without treatment application is shown in Figure 4. Higher emissions are generally observed from the treatment plot than from the control plot. In addition, the range in observed emissions from the treatment plot is generally larger than the range observed in the untreated plot.

Figure 5 shows box plots for N2O emissions from the conventional agricultural plots. Daily average N2O emissions are much more variable and intermittent over the study period. Unlike CO2, there is no clear trend showing a significant difference between high and none treatment application. Additionally, the data suggest that N2O emissions were generally higher in June than in July. This is likely a response to fertilizer application that occurred in June, whereas no fertilizer was applied to the plots in July.

Figure 4. Box plot comparing the range and average carbon dioxide flux for both the treated (yellow) and untreated (teal) conventional agriculture plots.
Figure 5. Box plot comparing the range and average nitrous oxide flux for both the treated (yellow) and untreated (teal) conventional agriculture plots.

A comparison between CO2 measured with the manual soil gas flux chambers and the continuous data collected with the eosAC automated chambers is shown in Figure 6. Overall there is good agreement between the two methods, particularly for the untreated plot. The manual chamber measurements were collected from this particular plot when emissions were at their lowest point during the deployment period. Recall that sampling all 36 plots takes ~12 hours. This makes direct comparisons between plots difficult because diurnal patterns can vary widely within a given individual plot over a 12 hour window. This helps illustrate the importance of accurately characterizing diurnal patterns when examining treatment effects on CO2 emissions.

In general, manual chamber measurements underestimated emissions compared to the automated chamber measurements (example shown in Figure 7). Using the automated chamber data, the daily average N2O emissions from this deployment were 5.6 nmol/m2/s and 3.2 nmol/m2/s for the untreated and high treatment plots respectively. Therefore, extrapolating daily average emissions from this set of manual measurements would have underestimated the true average by approximately 50%.

Figure 6. Comparison between carbon dioxide flux measurements collected using manual chambers (×) and continuous data collected using eosAC automated chambers (lines).
Figure 7. Comparison between nitrous oxide measurements collected using manual chambers (×) and continuous data collected using eosAC automated chambers (lines).


The eosAC automated chamber system enabled Dr. Saikawa to characterize diurnal patterns in CO2 emissions at her field site. This was especially important for comparing and analyzing data from the 36 plots that were sampled using manual chambers over a 12 hour period. Without an understanding of diurnal patterns, determining effects of varying treatment levels and agricultural practices would be extremely difficult and prone to uncertainty.

The comparison between manual and the eosAC automated chamber measurements also highlights the importance of continuous data for estimating daily average N2O emissions. Dr. Saikawa and her team found that the manual chamber measurements underestimated the true daily average N2O emissions by ~50%. In addition, manual chamber measurements provided no information about the range in daily emissions. By using the automated chamber system, Dr. Saikawa was able to more accurately estimate annual N2O budgets at her field site.

What's Next?

The findings presented in this case study are preliminary, but you can get future updates on this project and other by visiting Dr. Saikawa’s website and by following her on twitter.

You can also learn more about this project by checking out this webinar presentation by Dr. Saikawa.


Thanks to Eri Saikawa of Emory University for sharing her photos, performing the measurements and analyzing the data associated with this study.