First-Ever Conceptual Model Explains Variations in Agricultural N2O Emissions

An autochamber collecting data during the middle of growing season. Yang’s team gathered data at different growth stages to determine what caused varying N2O emissions in flat agricultural fields. Photo Credit: Will Eddy

Nitrous oxide (N2O) has long been agriculture’s sustainability Achilles heel. While only making up 6% of U.S. greenhouse gas (GHG) emissions, N2O has 300 times the heat-trapping ability ofcarbon dioxide (CO2) and stays in the atmosphere for about 100 years.

This greenhouse gas is produced by soil microbes whose activity depends on highly variable soil conditions. This variability makes it difficult to accurately measure annual N2O emissions at the field scale, complicating scientists’ ability to reduce those emissions using climate-smart agricultural practices.

Professor Wendy Yang, UIUC

“N2O emissions are notoriously variable in both time and space,” said Wendy Yang, Professor of Plant Biology at the University of Illinois Urbana-Champaign. “If you measure emissions today and you go back out to the field tomorrow, you could get something very, very different. If you take a measurement in one spot then take three steps to the right, you could get very different results.”

Yang, Associate Director of the Agroecosystem Sustainability Center (ASC) in the Institute for Sustainability, Energy, and Environment (iSEE) at Illinois, co-authored a recent paper published in Communications Earth and Environment addressing this very issue. The study proposed the first conceptual model — the “cannon model” — that explains N2O’s extreme spatial variationswithin agricultural fields that appear to have quite homogenous soil conditions. It is one of the latest papers from SMARTFARM Phase II, an ASC project supported by the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E) program that developed an innovative system-of-systems modeling approach to monitoring, reporting, and verification(MRV) of greenhouse gas emissions.

The ingredients to make N2O are well known. Nitrate, water, and dissolved organic carbon(DOC) — largely derived from dead plant material — are all that certain soil microbes, called denitrifiers, need to produce nitrous oxide. “Trigger” events like rainfall, fertilization, or spring thaw can increase the availability of these ingredients in the soil to temporarily spike N2O emissions. 

Before the cannon model, scientists had trouble explaining why different locations withinagricultural fields responded differently to these triggers. In hilly landscapes, low-lying areaswhere N2O ingredients converge tend to trigger higher emissions. However, considerable variation in emissions also occurs in fields with flat landscapes. “We didn’t think that topography was the full story,” Yang said. 

To get the full story, the research team took measurements from two farms managed by different farmers growing maize using different tillage and fertilization practices. “Most studies attempt to minimize variation to detect treatment effects,” Yang said. But in this case, Yang’s team scattered equipment that could automatically record hourly N2O emissions to measure variation from different environmental conditions and plants at different growth stages over the growing season.

They discovered that the abundance of nitrate, water, and DOC in the soil differed between locations in each field that had consistently low N2O emissions versus locations that episodically experienced high emissions. This caused different controls on N2O emissions that could explain why some areas of the fields do not respond to emission triggers. This finding led to the creation of the “cannon model.”

A rainbow rises over three autochambers collecting data in a Central Illinois field. This data informed the creation of the new conceptual “cannon model.” Photo Credit: Will Eddy

What the cannon model illustrates is that, according to Yang, “the amount of nitrate and DOC in the soil constrains nitrous oxide emissions in locations with consistently low emissions and, in places where those resources are more abundant, soil moisture controls N2O emissions.”

Just like firing off a real cannon, Yang’s cannon model — whose final blast is N2O emissions —requires ammunition (soil nitrate), gunpowder (DOC), and a spark to light the fuse (soil moisture). “High soil moisture, essentially the water that’s in the soil, pushes gas out of the soil pores,” Yang said. “It allows anaerobic soil microbes to produce nitrous oxide when there is sufficient nitrate and DOC available. These rainfall events are essentially like lighting the ignition.” 

By acknowledging that sections of a field can have different availability of ingredients that control N2O production, this conceptual model can lead to more accurate predictions of how much N2O is released. Only where more cannonballs and gunpowder are available can the cannon be fired by high soil moisture, leading to more N2O emitted.

The ability to better predict where high emissions of N2O occur in agricultural fields leads toopportunities for further research and reducing GHG emissions. The next steps, according to Yang, are to conduct more research on why DOC and nitrogen are unevenly distributed across a field and replicate the study on fields with different slopes and soil types.

Data from this study will be used to better refine the accuracy of ecosystem models. “SMARTFARM Lead PI Kaiyu Guan combines ‘ecosys’ — a process-based model that mechanistically represents plant physiological processes, microbial processes, and physical processes — with machine learning approaches,” Yang said. “Our data and our understanding of these processes are being passed on to Kaiyu’s group to improve the model.”

Gabrielle Jackson, summer 2024 intern with Microbial Interactions Create Research Opportunities for Community College Students (Micro-CCS), works in the field. Jackson is working on a follow-up study “to help us better understand why soil nitrate and dissolved organic carbon vary in abundance across the field,” according to Yang. Photo Credit: Will Eddy

Especially with precision agriculture on the rise, further research and more advanced models could provide valuable data to farmers looking to reduce their GHG output without compromising their income. “We have to use models to simulate all of the different weather and soil conditions that could occur across the Corn Belt and predict the potential for N2O emissions reductions with different climate-smart practices,” Yang said. “Then we can make better recommendations to farmers based on their specific land.”  

Lead authors on the paper were two members of Yang’s lab, former Postdoc Ziliang Zhang of iSEE and former Visiting Research Scientist William Eddy of the School of Integrative Biology. Co-authors included former Postdoc Emily Stuchiner of iSEE and SMARTFARM Co-PI and Professor Emeritus Evan DeLucia of ASC, iSEE, and Plant Biology.

To incentivize farmers to adopt environmentally beneficial practices, carbon credits are awarded to those who demonstrate practices that draw more carbon into the soil from the atmosphere. However, there is currently a lack of confidence that soil organic carbon credits represent real climate benefits. A research project led by Eric Potash and the Agroecosystem Sustainability Center at the University of Illinois, has shown that a more rigorous approach to soil carbon quantification is possible, one which promises to build confidence in credits representing real climate benefits.

Currently the most common approach to quantifying these soil carbon credits is called “measure-and-model.” In this approach a soil carbon project developer will measure the carbon stocks on their farms before they begin changing the practice, then they will run models on a computer to estimate the change over time.

By contrast, in the “measure-and-remeasure” approach studied by Potash and co-authors, developers measure those carbon stocks before the practice and then go back a few years later to remeasure stocks. This empirical approach can provide more reliable quantification of soil carbon accrual. Yet voluntary carbon markets and other carbon accounting approaches, including national-level greenhouse gas accounting, rely primarily on measure-and-model because of an assumption that the direct measurement approach is too expensive at landscape and regional scales.

Potash along with co-authors Mark Bradford from the Yale School of the Environment, Emily Oldfield from the Environmental Defense Fund, and ASC Director Kaiyu Guan show that measure-and-remeasure can be economically feasible for carbon crediting when a project is scaled up. The team has developed a web app, where developers can plug in a number of variables to determine how much it would cost to implement measure-and-remeasure in their projects and how profitable they can be selling carbon credits.

Instead of using biogeochemical modeling as in the measure-and-model approach, Potash and co-authors use a multilevel statistical model to estimate the costs and benefits of measure-and-remeasure. In this approach, the group estimated how much sampling would need to be done under the more rigorous measure-and-remeasure to precisely quantify the overall effect of climate-smart practices across a large number of fields. Prior academic work on soil carbon measurement hasn’t considered projects on the scale of thousands or tens of thousands of fields that occur in the voluntary carbon market.

Prior to this work, there was a perception of an inherent trade-off between rigor and economic feasibility that led most developers to take up the cheaper but less rigorous measure-and-model. In this research, Potash and co-authors have provided a framework that factors in a host of variables (all the costs and all the benefits), and shows that larger projects can be developed under the measure-and-remeasure approach and still be quite profitable. The web app enables users to interactively explore how these variables affect the economics of their specific SOC projects. Small projects can also use the app to efficiently design soil carbon measurement efforts, though they may not be profitable in the carbon market.

Figure from research showing how economic feasibility is a function of number of fields (project size), carbon price, and carbon accrual (average treatment effect). Figure credit: Eric Potash

“Ultimately the goal is to incentivise these practices,” Potash said. “There is a huge perceived opportunity to reduce carbon emissions from agriculture and build the health of soils. At the moment, projects are being developed with measure-and-model, but we aren’t confident in their benefits. Before this research, it felt like we didn’t have another option. However, we found that there is a better way forward. Measure-and-remeasure can be economical. We think it will help to build confidence in soil carbon accounting more generally, and not just for carbon markets.”

Primary media contacts: Kaiyu Guan (kaiyug@illinois.edu), Eric Potash (epotash@illinois.edu)

Measure-and-remeasure as an economically feasible approach to crediting soil organic carbon at scale.
E Potash, M Bradford, E Oldfield, and K Guan. Environmental Research Letters. 20 (2025) 024025