Category: Agroecosystem Sustainability Center

It made headlines nationwide. An abrupt dust storm blinded drivers on Interstate 55 south of Springfield, Ill., on May 1, causing a massive pileup, ultimately killing eight people and injuring 37. A team of researchers from the University of Illinois, Cornell University, and Texas A&M University will try to determine the factors that caused the tragic event with the hope to prevent future similar episodes thanks to a U.S. Department of Agriculture (USDA) grant.

“We put this team together to understand the mechanisms of this kind of dust storm through the lens of at least three factors,” said Sheng Wang, the principal investigator (PI) on the project. “How much did climate (drought, soil dryness), farming activities (tillage, cover crop planting), and the extreme weather event (wind gusts, direction, etc.) each contribute to the disaster?”

Wang notes that while the May 1 I-55 storm received the most attention, it was one of a multitude of such events to hit the Midwest within the last year.

“Agricultural dust storms very rarely happen in the central Midwest,” Wang said. “Not since the Dust Bowl of the 1930s have we seen them here with this kind of regularity.”

Wang, a research assistant professor and research scientist affiliated with the Institute for Sustainability, Energy, and Environment (iSEE) and the Department of Natural Resources and Environmental Sciences (NRES), leads the airborne sensing research team for the two-year old Agroecosystems Sustainability Center (ASC).

ASC, under the leadership of Founder and Director Kaiyu Guan, was established to use advanced modeling and monitoring of agroecosystems to improve sustainability in the light of climate change.

“We have the foundation to elaborate work on these problems,” Wang said. “For example, we can use remote system data as well as artificial intelligence to detect conservation practices like the use of cover crops. We have published a number of papers on this subject.”

The USDA has funds available to support “rapid response” projects. In this case, researchers had one month after the incident to apply for the funding and a year to produce results. The study begins in earnest on Oct. 1, 2023 and concludes Sept. 30, 2024.

Team members will first use remote system data collected from satellite images and ASC’s highly accurate AI modeling to characterize conservation practice in Illinois. They will use information along with NOAA climate data to plug into weather research and forecasting with chemistry (WRF-Chem) models. From there they can develop different scenarios. For example, what if farmers had used 10 percent or 50 percent of their land for cover crops.

“We can develop a mitigation strategy to advise stakeholders on potential preventative measures like cover crops and no-till practices,” Wang said.

The third component will be a social and policy analysis. The researchers do not want to merely rely on empirical data to drive the solution. They intend to survey local farmers, policymakers, and traffic agencies to understand their thoughts about the dust storms and conservation agriculture practices.

“At ASC we advocate for these practices,” Wang said. “We can use modeling of these incidents as part of our research at UIUC to help develop agriculture policy and social science.”

The team was carefully put together with the goal of using multiple approaches to understand the mechanism of this kind of dust storm. Guan and Bin Peng, an incoming assistant professor of crop sciences, will focus on sustainability. Jonathan Coppess, the Director of the Gardner Agricultural Policy Program, and Mackenzie Johnson, an assistant professor in NRES, will focus on agricultural policy. All of which are a vital part of the ASC team. Qi Li, an assistant professor of civil and environmental engineering at Cornell University and Yangyang Xu, an assistant professor of atmospheric sciences at Texas A&M University, will largely focus on modeling.

“We have received very positive feedback from the USDA,” Wang said. “ There are many dust storm studies on the dry land region in the western part of the United States, but there is very limited study in the Midwest. This is a great integration of research with an extension component. It takes advantage of the strength of ASC and has great interest to the public. With such an elite team, I am quite confident we can generate some very exciting results.”

— Article by ASC Communications Lead Mike Koon

Kaiyu Guan, Founding Director of the Agroecosystem Sustainability Center (ASC) and a Blue Waters Associate Professor of Natural Resources and Environmental Sciences, has received the prestigious James B. Macelwane Medal from the American Geophysical Union (AGU).

The award is given annually to three to five early career scientists in recognition of their significant contributions to Earth and space science. Honorees automatic distinction as AGU Fellows. The Macelwane Medal  is named in honor of the former AGU president James B. Macelwane, who was renowned for his contributions to geophysics.

Guan joins other scientists, leaders, educators, journalists, and communicators from around the world who have made outstanding achievements and contributions by pushing forward the frontiers of science. According to the AGU, “Each recipient embodies the AGU’s community’s shared vision of a thriving, sustainable, and equitable future powered by discovery, innovation, and action.”

Guan founded and directs ASC, which has a mission to revolutionize agricultural systems through research, collaboration, and engagement, bridging science and practice for agricultural productivity and ecosystem sustainability.

He leads a research group focusing on the computational modeling and sensing of agricultural ecosystems under climate change. His work combines advanced domain knowledge with satellite data, supercomputing, process-based modeling, and machine learning.

In doing so, he addresses key questions on how climate and human management control productivity and ecosystem services for agricultural systems. He and his team have made significant breakthroughs towards quantifying the impact of environmental stresses and human activities on agricultural productivity and sustainability.

Guan earned a Ph.D. from Princeton University in 2013 and was a postdoctoral scholar at Stanford University. The AGU previously honored Guan with its Early Career Award in Global Environmental Change.

“I am very honored to receive the James B. Macelwane Medal and to be included alongside such distinguished previous recipients,” Guan said. “This is a shared honor to our whole team, including our students, researchers, collaborators, and mentors. I am truly grateful for all their great contributions. We are striving to build solutions to make our agricultural system both more productive and more sustainable. It is a very hard problem and requires a big team effort. I thank AGU for giving us this recognition in this uphill journey, and we know the hard work needs to continue and accelerate.”

AGU will formally recognize this year’s recipients at AGU23, which will convene more than 25,000 attendees from over 100 countries in San Francisco and online everywhere on Dec. 11-15.

About the American Geophysical Union: The American Geophysical Union (AGU) supports a global community of more than half a million professionals and advocates in the Earth and space sciences. Through broad and inclusive partnerships, AGU aims to advance discovery and solution science that accelerate knowledge and create solutions that are ethical, unbiased and respectful of communities and their values.

About the Agroecosystem Sustainability Center: ASC was established in 2021 to be a global leader in harmonizing sustainable food production with thriving ecosystems. The Center, made up of a cross-disciplinary set of faculty and researchers at the University of Illinois at Urbana-Champaign, strives to revolutionize agricultural systems through research, collaboration, and engagement, bridging science and practice for agricultural productivity and ecosystem sustainability.

AGU press contact:  Samson Reiny, (202) 998-8654, news@agu.org

ASC press contact: Mike Koon, (217) 898-3519, mkoon@illinois.edu

A study led by researchers at the Agroecosystem Sustainability Center (ASC) at the University of Illinois Urbana-Champaign provides new insights for quantifying cropland carbon budgets and soil carbon credits, two important metrics for mitigating climate change.

The results, outlined in a paper published in the soil science journal Geoderma, could simplify the process for calculating soil carbon credits, which reward farmers for conserving soil carbon through crop rotation, no-tillage, cover crops, and other conservation practices that improve soil health. The project was funded by the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E).

Agricultural activity causes a significant amount of soil organic carbon (SOC) to be released into the atmosphere as carbon dioxide, a greenhouse gas that contributes to climate change. Several conservation practices have been suggested to help sequester that carbon in the soil, but their potential to enhance the total SOC in a soil profile, known as SOC stock, needs to be assessed locally. Such assessments are key to the emerging agricultural carbon credit market.

Accurately calculating cropland carbon budgets and soil carbon credits is critical to assessing the climate change mitigation potential of agriculture as well as conservation practices. Those calculations are sensitive to local soil and climatic conditions, especially the initial SOC stock used to initialize the calculation models. However, various uncertainties exist in SOC stock datasets, and it’s unclear how that can affect cropland carbon budget and soil carbon credit calculations, according to lead author Wang Zhou, Research Scientist at the ASC and the Department of Natural Resources and Environmental Sciences (NRES) at Illinois.

In this study, researchers used an advanced and well-validated agroecosystem model, known as ecosys, to assess the impact of SOC stock uncertainty on cropland carbon budget and soil carbon credit calculation in corn-soybean rotation systems in the U.S. Midwest.

They found that high-accuracy SOC concentration measurements are needed to quantify a cropland carbon budget, but the current publicly available soil dataset is sufficient to accurately calculate carbon credits with low uncertainty.

“This is a very important study that reveals counter-intuitive findings. Initial soil carbon data is very important for all the downstream carbon budget calculation. However, carbon credit measures the relative soil carbon difference between a new practice and a business-as-usual scenario. We find that the uncertainty of initial soil carbon data has limited impacts on the final calculated soil carbon credit,” said ASC Founding Director Kaiyu Guan, Blue Waters Professor in NRES and the National Center for Supercomputing Applications (NCSA) at Illinois and lead of the DOE-funded SMARTFARM project at iSEE, which featured several co-authors on this paper.

The results indicate that expensive in-field soil sampling may not be required when focusing only on quantifying soil carbon credits from farm conservation practices — a major benefit for the agricultural carbon credit market.

“Uncertainty in SOC concentration measurements has a large impact on cropland carbon budget calculation, indicating novel approaches such as hyperspectral remote sensing are needed to estimate topsoil SOC concentration at large scale to reduce the uncertainty from interpolation. However, uncertainty in SOC concentration only has a slight impact on soil carbon credit calculation, suggesting solely focusing on quantifying soil carbon credit from additional management practices may not require extensive in-field soil sampling — an advantage considering its high cost,” Zhou said.

“The approach in this study can be applied to other models and used to assess important uncertainties of the carbon sequestration potential of various conservative land management practices,” said Bin Peng, the other primary author of the study and Senior Research Scientist at ASC and NRES.

The ASC was jointly established by the Institute for Sustainability, Energy and Environment (iSEE), the College of Agricultural, Consumer and Environmental Sciences (ACES), and the Office of the Vice Chancellor for Research and Innovation at Illinois.

Co-authors on the study included ASC Associate Director Andrew Margenot, Assistant Professor of Crop Sciences; DoKyoung Lee, Professor of Crop Sciences and ASC founding faculty member; Even DeLucia, Professor Emeritus of Plant Biology and ASC founding faculty member; Sheng Wang of ASC and NRES Research Assistant Professor; Ziqi Qin of ASC and graduate student in NRES; NRES Professor Michelle Wander; Jinyun Tang, Staff Scientist of the Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory; Zhenong Jin, Assistant Professor in the Department of Bioproducts and Biosystems Engineering, University of Minnesota; and Robert Grant, Professor in the Department of Renewable Resources, University of Alberta, Edmonton, Canada.

— iSEE Communications Specialist Julie Wurth

There is much more carbon stored in Earth’s soil than in its atmosphere. A significant portion of this soil carbon is in organic form (carbon bound to carbon), called soil organic carbon (SOC). Notably, unlike the inorganic carbon in soils, the amount of SOC, and how quickly it is built up or lost, can be influenced by humans. Since its advent about 10,000 years ago, agriculture has caused a significant amount of SOC to be released into the atmosphere as carbon dioxide, contributing to climate change. 

Quantifying the amount of SOC in agricultural fields is therefore essential for monitoring the carbon cycle and developing sustainable management practices that minimize carbon emissions and sequester carbon from the atmosphere to the soil to reduce or reverse the climate effects of agriculture.

“Accurate and efficient SOC estimation is essential,” said Eric Potash, a Research Scientist in the Agroecosystem Sustainability Center (ASC) and Department of Natural Resource & Environmental Sciences (NRES) at the University of Illinois Urbana-Champaign. “Governments need to estimate SOC in order to implement policies to minimize climate change. Researchers need to estimate SOC to develop sustainable management practices. And farmers need to estimate SOC to participate in emerging carbon credit markets.”

The traditional and most reliable way to quantify SOC is by soil sampling, with analyses in the lab (“wet chemical” measurement). But which locations in the field should be sampled? And how many samples should be taken for an accurate estimate? Each additional soil core adds significant labor and expense — and uncertainties in how to optimize sampling can lead to substantial extra costs.

In a new publication from the U.S. Department of Energy’s (DOE) SMARTFARM Project, Potash and other SMARTFARM researchers evaluated strategies for estimating SOC. Their goal was to develop an estimation strategy that maximizes accuracy while minimizing the number of soil cores sampled. 

The SMARTFARM Project, a program led by co-author and Blue Waters Professor in NRES Kaiyu Guan and funded by the DOE’s Advanced Research Projects Agency-Energy (ARPA-E), endeavors to develop a precise solution for measuring and quantifying greenhouse gas emissions and SOC change during the production of crops.

“We aim to collect gold-standard ground truth data and also to develop new technology to quantify field-level carbon outcomes for bioenergy crops, improving yield and also improving environmental sustainability,” said Guan, ASC Founding Director.

This work is made possible with unprecedented data collection effort.

“We have collected 225 soil samples at 3 samples per acre at one of the SMARTFARM sites. The samples were collected up to 1 meter deep using a Giddings probe. This level of dense sampling has never been done before,” said co-author DoKyoung Lee, a Professor of Crop Sciences, a co-PI of the SMARTFARM project, and also an ASC founding faculty member.

In this work, the researchers approached the problem by evaluating the two steps involved in estimating SOC: (1) deciding where in a field to take soil samples; and (2) deciding on a statistical rule for calculating an estimate (called an estimator). By using a commercial field in central Illinois that had been intensively sampled to measure SOC, a variety of strategies could be evaluated for their performance in estimating SOC in the field. 

The researchers found that in a typical Midwestern agricultural field, they can leverage publicly available soil surveys and satellite imagery to efficiently select sample locations. This should reduce the number of samples needed to achieve a given accuracy of SOC quantification by about 28% compared to selecting sampling locations at random.

“For researchers and agencies monitoring SOC stocks, this study offers a strategy to increase accuracy, supporting cost optimization of sampling methods,” said co-author Andrew Margenot, Crop Sciences Assistant Professor and ASC Associate Director.

“Future studies can use these findings both as a benchmark against which to compare new SOC stock estimation strategies and as a demonstration of how to evaluate those strategies,” Potash said. 

The research team is currently collecting data from many more fields to test the ability to generalize their findings — as well as to develop further improvements to SOC estimation strategies. Team members are also developing a software tool to make their improved sampling methods available to farmers and researchers.

In addition to Potash, Guan, Lee, and Margenot, co-authors on this publication include Evan DeLucia, ASC and Professor Emeritus of Plant Biology; Sheng Wang, ASC and NRES Research Assistant Professor; and Chunhwa Jang, Crop Sciences Postdoctoral Researcher. Read the full article in Geoderma >>>

ASC was jointly established by the Institute for Sustainability, Energy and Environment (iSEE), the College of Agricultural, Consumer and Environmental Sciences (ACES), and the Office of the Vice Chancellor for Research and Innovation at the University of Illinois Urbana-Champaign. NRES and Crop Sciences are in the College of ACES, and Plant Biology is in the School of Integrative Biology, part of the College of Liberal Arts & Sciences at Illinois.

— News release by April Wendling, iSEE Communications Specialist

Carbon is everywhere. It’s in the atmosphere, in the oceans, in the soil, in our food, in our bodies. As the backbone of all organic molecules that make up life, carbon is a very accurate predictor of crop yields. And soil is the largest carbon pool on earth, playing an important role in keeping our climate stable. 

As such, computational models that track carbon as it cycles through an agroecosystem have massive untapped potential to advance the field of precision agriculture, increasing crop yields and informing sustainable farming practices.

“Although modeling the carbon cycle in agroecosystems has been done before, our work represents the most comprehensive integration of models and observations, as well as rigorous validation that includes rich measurements from both field and regional scales. The modeling performance of our solution (published this month in Agricultural and Forest Meteorology) far surpasses prior studies,” said Kaiyu Guan, an Associate Professor of Natural Resources & Environmental Sciences at the University of Illinois Urbana-Champaign. Guan is also a Blue Waters Associate Professor at the National Center for Supercomputing Applications (NCSA) and Founding Director of the Agroecosystem Sustainability Center created by the College of Agricultural, Consumer and Environmental Sciences and iSEE.

The carbon cycle in agroecosystems can be generalized into three main carbon fluxes that travel to and from the plants and soil. Carbon enters the system through photosynthesis. Some leaves the system via plant respiration and soil respiration, while carbon in the form of grain and biomass is removed when crops are harvested. In principle, the sum of these fluxes is equal to the net carbon movement through the system — and that net change, especially over long periods of time, is what contributes to change in an agroecosystem’s soil organic carbon.

Soil organic carbon (SOC) is exactly what it sounds like: carbon in the form of organic molecules in the soil. Generally speaking, the greater a field’s SOC, the more productive it will be. However, in the U.S. Midwest’s croplands, about 30-50% of SOC has been lost since their cultivation began. This loss of SOC may enhance the risk of decreases in crop yield, especially under future climate conditions.

Members of Guan’s SMARTFARM Project team used an advanced agroecosystem model named ecosys, which contains the most complex mechanisms for simulating the energy, water, carbon, and nutrient fluxes cycling in the agroecosystem. This model was originally developed by Professor of Ecosystem Modelling Robert Grant from the University of Alberta. Over the past few years, Guan’s team has made continuous efforts toward building a solution to further constrain the ecosys model with massive observational data.

The researchers used an innovative “model-data fusion” approach, which integrates advanced model simulations with observational data. This approach allowed them to validate model simulation results, constrain uncertain model parameters, and ensure that the model emulates the processes driving the carbon cycle at all stages. Multiple types of datasets were used, like eddy covariance flux tower data, which is widely regarded as the gold standard for landscape-scale measures of carbon; USDA crop yield data that provides the harvested carbon; and novel satellite data that provides photosynthesis observations.

“Additionally, we used detailed carbon allocation data measured over 10 years,” said lead author Wang Zhou, a Postdoctoral Research Associate. “That’s the data that tells you where a plant allocates the carbon it takes in from photosynthesis — how much goes to the stem, how much to the roots, how much to the leaves.”

“What really makes our modeling solution exciting,” Guan said, “is that we use the most advanced observations from satellites to constrain a powerful agroecosystem model, and we demonstrate that this can achieve the highest performance in estimating different carbon components.” Early this year, Guan and Research Scientist Chongya Jiang developed an algorithm to estimate photosynthesis from satellite data. This newly available photosynthesis data across every corn and soybean field in the U.S. Midwest was also used to validate and constrain the model to ensure the team can accurately reproduce the observed photosynthesis from satellite and the USDA-reported crop yield, as well as their responses to environmental variability.

“Integrating satellite observations with a process-based model like ecosys is the key to ensure the accuracy of our solution, and more importantly, the potential of using our modeling solution at a new location, such as South America or Africa,” Research Scientist Bin Peng said.

With so many moving parts, a huge amount of time and effort has gone into the development of this model-data fusion solution. Guan’s team is proud to release the first paper on the model in Agricultural and Forest Meteorology, and the researchers have a couple of other papers using this method in the works. For instance, in another recent study involving Guan’s team and led by the University of Minnesota, the researchers integrated their ecosys-simulated results with artificial intelligence to estimate N₂O emission from the U.S. Corn Belt. This study was published in Environmental Research Letters.

“This is state of the art for quantifying carbon budget and credit,” Guan said. “We want to show people what is possible and set a high standard going forward. We let rigorous science speak for itself. I believe that’s the most powerful way to say things as scientists.”

Guan’s SMARTFARM Project, a program funded by the U.S. Department of Energy, is focused on pioneering the technology to quantify field-scale carbon credits for U.S. farmland. The team’s ambition is to use this developed model-data fusion method as the foundation to accurately quantify the carbon budget at any scale, and also support smart management at the farm scale. Through precision agriculture, they hope to help farmers not only maximize their yields, but also better sustain their land and its SOC content.

Various funding agencies have supported Guan’s team over the years, including the National Science Foundation Career Award, the Foundation for Food and Agriculture Research, DOE Advanced Research Projects Agency-Energy SMARTFARM program, NASA Carbon Monitoring System Program, and USDA National Institute of Food and Agriculture.

In addition to Guan, Grant, Zhou, Jiang, and Peng, co-authors on this latest publication include Jinyung Chang, Lawrence Berkeley National Laboratory; Zhenong Jin, University of Minnesota; and Symon Mezbahuddin, University of Alberta. Read the full article in Agricultural and Forest Meteorology >>>

— News release by April Wendling, iSEE Communications Associate