Greenhouse gas (GHG) emissions, notably nitrous oxide (N₂O), methane (CH₄), and carbon dioxide (CO₂) are known to be the biggest contributors to climate change in terms of atmospheric warming. A large proportion of these GHGs, plus ammonia (NH₃), are emitted from agricultural soils. Finding ways to reduce agriculture-induced emissions of GHGs and ammonia has a large potential to mitigate climate change and air pollution. 

This blog will describe how the combination of the Picarro G2508 five-species gas concentration analyzer and Eosense’s eosAC Automated Soil Flux Chamber is helping researchers understand and advance climate-smart agriculture. 

Use of Natural Methods in Agriculture to Reduce GHGs
Professor Eri Saikawa, Emory University, in Atlanta Georgia, has been researching the use of different cover crop systems to reduce the need for synthetic nitrogen fertilizer, and so in effect, reduce GHG emissions coming from agricultural soil. 

Her research focuses on agricultural practices for growing corn since it is the largest user of synthetic nitrogen fertilizer. She has been comparing GHG emissions using bare soil/no cover crop, and cover crops such as white clover(called living mulch), cereal rye, and crimson clover (Figure 1). 

 
Figure 1.  Agricultural Practices and GHG Emissions

Dr. Saikawa was interested in testing white clover and crimson clover as cover crops because they are both nitrogen fixers.The living mulch system used a perennial plant in which the white clover remained alive and regrew over winter. She wanted to determine if these systems, and especially the living mulch, could reduce the need for synthetic fertilizer. All cover crops were planted after harvest over the winter. The cereal rye and crimson clover were killed before the planting of corn. For the living mulch system, the white clover is killed in strips where the corn is then planted.  

Field Methodology
The field studies were conducted in Georgia, on the University of Georgia’s agricultural experimental farm. 

In 2018, her team used the Picarro G2508 analyzer and manual chambers to calculate the accumulation of N_²O, CH₄, CO₂, and NH₃ gases and fluxes over time in these four systems. In 2021, she measured GHG emissions from six plots using the G2508, both manual chambers, and Eosense’s eosAC automated chambers. 

Differing nitrogen treatments were applied for each of the practices. They ranged from 250 kg/ha for bare soil to 50 kg/ha for living mulch. One of the first things she observed was the significant difference in soil health among the plots. Please refer to Figure 2.
 

Figure 2.  Improved Soil Health

Soil emission and flux measurements yielded useful results. With the Picarro/manual chamber system used in 2018, Dr. Saikawa observed that at the beginning of the season there were higher CO₂ fluxes from the living mulch but not much difference at the end. As expected, N₂O fluxes rose right after fertilization of the bare soil and cereal rye. But surprisingly, there were also fluxes later in the season when the living mulch was dying. For CH₄, the soil acted as a sink at the beginning and end of the season, with the bare soil acting as less of a sink than the soil of the cover crop. 

Using the data from the measurements to calculate the net carbon equivalence (CE) of each agricultural practice, she found that the living mulch had the lowest net CE, indicating that the living mulch practice could potentially be used to reduce GHGs. 

Research continued in 2021 on three agricultural practices - bare soil, cereal rye, and intercrop (corn with soybeans). Each of these field types were divided into sections with varying degrees of active pesticide and herbicides (no, low, and high). The research also included four replicates for each of the three crop types. Refer to Figure 3 for the field plot layout in 2021.

Figure 3. Field Plots in 2021

Measurements using manual chambers and the G2508 were conducted on the plots each week. However, it took ~12 hours using manual chambers to get flux measurements from each plot.

Measurements using the Eosense automated chambers and the Picarro G2508 were collected over ~24-36 hours on a weekly basis, which allowed her team to collect multiple flux measurements over time. The density of data was much, much higher with the automated system and it allowed her to see changes in the emission pattern over the 36-hour deployment period -- which was not possible using manual chambers.

Comparing Continuous and Manual Gas Measurement Results
Due to the fact that the manual measurements only yielded four flux measurements per week, the number of data points was much lower compared to the automated measurements. Some unexplained extremely high outliers were detected in the manual method which were not replicated by the automated data set. Now to look at the gas monitoring results, CO₂ release did not differ widely over time in the cereal rye and the intercrop plots. 

Right after fertilization, a large burst of N₂O was detected in both the automated and the manual methods, as was expected. The N₂O release measured very high (overestimated) in the manual method compared to a consistent increase in release measured with the automated chambers.  
Methane results correlated well between the automated and manual measurements. The data from these methods showed that the soils appeared to serve both as a sink and a flux for methane. 

Ammonia bursts were measured right after fertilization and again after irrigation in the continuous set up. The manual measurements couldn’t be taken during irrigation, so there is a gap in the timing of the data during manual measurements i.e., some of the bursts measured in the continuous method were not captured in the manual method. Again, since the manual measurements were taken during the morning, those samples missed the bursts shown later in the day found in the automated method.

A full review of the data obtained during the 2018, and 2021 field work is available in the webinar “Quantifying GHGs and Ammonia Fluxes in an Experimental Farm with Automated and Manual Measurements.”

Conclusions and Recommendations
The research team would like to further evaluate the timing for obtaining manual samples as there is a potential to overestimate or underestimate daily averages from this method.  In addition, the southeastern United States is lacking data on GHG emissions from agricultural practices, especially corn crops. Comparing results using automated measurements over a 36-hour period vs manual measurements once a week, showed that emissions can vary greatly depending on the time of day and time during the growing season, especially right after fertilization and irrigation.

By being able to take frequent measurements over time, and knowing when to take measurements, researchers can get more accurate data on how the different farming practices affect GHG and ammonia emissions, and can develop better models that can be used in the future to promote more effective climate-smart agricultural practices.