Projects

1.  Transdisciplinary, deeply based on mechanistic understanding
2.  System view: earth system science, to study agroecosystem dynamics
3.  Carbon cycle science as an example

Scientists at ASC use a holistic and integrated systems approach to study the agroecosystem and build solutions towards sustainable agrifood systems. We aim to deepen our mechanistic understanding of the complex biophysical and biogeochemical processes in the agroecosystem through interdisciplinary studies. We use a systems approach to study the agroecosystem dynamics, especially the coupled carbon-water-nutrient dynamics. Comprehensive observations of different components in the agroecosystem, systems modeling, and model-data integration improve our systems-level understanding of the agroecosystem. We also use a system approach to integrate social, economic, policy aspects together to generate real-world solutions, for farming communities and policymakers.

Ground Sensors
Spatially-distributed and temporally-continuous ground data are prerequisite for understanding agroecosystems as well as interpreting remote sensing data. To meet this requirement, we have built and developed Fluospec2 solar-induced fluorescence and hyperspectral observation system, CropEYEs crop growth monitoring system, and FieldRover observation system.

Airborne Hyperspectral Imaging
A high-fidelity full-optical-range airborne hyperspectral system with a high-performance data processing pipeline collects and produces high-quality, high spatial and spectral resolution visible, near-infrared, and shortwave infrared surface reflectance for agroecosystem monitoring.

Satellite Sensing
Integration of multi-source and multi-modal spaceborne remote sensing data, such as multispectral, hyperspectral, solar-induced fluorescence, thermal infrared, microwave, LiDAR and gravity, to comprehensively understand energy-carbon-water-nutrient dynamics in agroecosystems.

Advanced Ecosystem Models: ecosys, CLM5
Ecosystem models are process-based models that simulate the dynamics of biophysical and biogeochemical processes in the ecosystem. The ecosystem models used at the ASC center include: (1) ecosys, an advanced ecosystem model with full representation of soil C-N-P cycles; and (2) CLM, the land component of U.S. flagship earth system model CESM.

Model-Data Fusion
Both observations and process-based modeling have their strengths and weaknesses. Systematic model-data fusion (MDF) is the most promising way for accurately quantifying agroecosystem outcomes through combining advantages from both observation and process-based modeling. Advanced techniques such as artificial intelligence and graphics processing unit (GPU) computing are fostering MDF to be scalable, accurate, and efficient.

EC Towers
An eddy covariance tower, also known as a flux tower, is a scientific instrument to measure the exchange of carbon dioxide, water vapor, and energy between the atmosphere and land surface at a high temporal resolution. The tower relies on the eddy covariance method, which involves measuring the fluctuations in wind speed, temperature, and gas concentration to calculate the flux of gasses and energy.

Chambers
Soil chambers are experimental apparatus to study soil processes by measuring the fluxes of gasses (such as carbon dioxide, methane, and nitrous oxide) between soil surface and the atmosphere. The chamber is usually placed over the soil, creating a sealed environment in which gas concentrations can be measured over time to determine the rate of gas exchange between the soil and the atmosphere.

N2O EC towers
An eddy covariance tower to measure the nitrous oxide exchange between ecosystems and the atmosphere.

1. Giddings probe
2. Isotope
3. Lab test/spectroscopy

1. knowledge-guided machine learning
2. computer vision and A

Team members: Shenlong Wang, Kaiyu Guan, Sheng Wang.                                                                                                             The ability to automatically build 3D digital replicas of plants and farmlands from images has countless applications in agriculture, environmental science, robotics, and other fields. However, 3D reconstruction of plants remains to be a challenging problem for computer vision due to heavy occlusion and the complex geometry of plant structures. The goal of this project is to leverage scientifically grounded knowledge on plant structure to build 3D reconstructions that are complete, accurate, and realistic. The development of such methods will enable automatic, large-scale monitoring of plants for agricultural and scientific purposes. The collected data can offer decision support for farmers while also aiding the design of new agricultural techniques, increasing crop productivity and alleviating the rising food crisis of today’s world. Plant shape reconstruction methods will also pave the way forward for intelligent manipulation of plants by autonomous robots, preparing them for tasks such as crop harvesting and pruning. Eventually, our goal is go even beyond shape and build models of mechanical and optical properties, bringing us closer and closer to truly complete digital twins of plants.

Team members: Kaiyu Guan, Bin Peng, Andrew Margenot, Gary Schnitkey

Recommendations on the optimal nitrogen rate can help farmers reduce the fertilizer cost while maintaining the high productivity, thus maximizing net economic returns and reducing nutrient loss. This project aims to maximize the NREC historical and current investment in N rate database development in order to better serve Illinois farmers in nutrient management. This project will compile the most comprehensive NREC N trial database and use the database to upgrade the MRTN calculator. The project will also develop a next generation MRTN calculator based on observation-constrained model simulations. The project will finally develop a web-based platform for both the upgraded and next-generation MRTN calculators to serve Illinois farmers and provide training, outreach, and education to farmers.

Team Members:
Andrew Margenot, McKenzie Johnson, Kaiyu Guan, Bin Peng

The U.S. Corn Belt produces about 30% of the world’s corn and soybean, which depends on the use of fertilizers containing both nitrogen (N) and phosphorus (P). However, heavy fertilizing directly affects farmers in terms of expenses and threatens valuable natural resources. Yet, farmers’ perceptions of nutrient management challenges vary widely as does their willingness to adopt novel nutrient management approaches. Thus, an urgent unmet need exists to provide farmers with and help them adopt effective and trusted nutrient management tools that are informed by advanced science and technology as well as by farmer-based practical expertise. This project addresses these needs by developing a smart and connected “Nutrient Management Community” (NuMC) in rural farming communities in the U.S. Corn Belt. Specifically, we will take an integrated approach using farmer-researcher-farmer knowledge transfers to co-produce an NuMC. Our primary goal is to develop science-driven recommendations on N and P management that can be tailored to different farmers’ needs.

Team Members: Kaiyu Guan, Bin Peng, Sheng Wang

This project aims to answer the following grand questions: could the U.S. Midwest remain as the global food basket in the next 100 years? How can we ensure co-sustainability of food production and environmental quality in this landscape? Carbon (e.g. crop growth), hydrology (both water quantity and quality), and nutrient cycles in the U.S. Midwest are closely intertwined from the field/headwater scale to the whole river network and continental scales. Human activities and practices do not singly affect one component, but the whole interconnections. Their complex feedbacks and non-linear interactions require a “system” view when assessing the U.S. Midwest landscape and potential adaptations. This project adopts a “system” view to holistically model and quantify the coupled “food-water-nutrient nexus” for the U.S. Midwest agroecosystems, aiming to significantly advance the process-based understanding and predictability of this agroecosystem, with two management practices (i.e. controlled drainage, and nutrient management) and under future climate conditions.

Team Members:
Kaiyu Guan, Bin Peng

Non-point pollution caused by agricultural management contributes to about 50% of nitrogen and 40% of phosphorus transported from the Mississippi River Basin to the Gulf of Mexico. Systems modeling of water and nutrient transport and reactions from field to watershed scale is urgently needed to guide the design of nutrient loss reduction strategies. However, existing models either oversimplify the hydrological and biogeochemical processes at farmland or are insufficient to capture the complex in-stream nutrient dynamics. Developing new field-to-watershed modeling capability would enable accurate quantification of water footprint for agricultural production under different management conditions. Moreover, model-data fusion and artificial intelligence can leverage existing and new sources of water quality data and other types of remote sensing data across different scales to support model development and validation. This project aims to develop a prototype for hydrological and water quality modeling from field to watershed scales targeting the US Midwest agroecosystems.

Team Members:
Kaiyu Guan, Bin Peng

Providing real-time, low-cost, and reliable information on irrigation requirements over large areas not only benefits individual producers through more efficient water use and less cost, but also creates long-term societal benefits through higher resource use efficiency, stronger water security / sustainability and rural economy. This project aims to build a decision support system for Nebraska to provide real-time monitoring and forecast for field-level crop irrigation requirements for row-crop producers based on center-pivot systems (i.e. timing and amount of irrigation), with a cyberinfrastructure that can disseminate decision-making information online with alerts by email and/or text messages to users.

Team Members: Kaiyu Guan, Bin Peng, Andrew Margenot, Jonathan Coppess, Gary Schnitkey

The intensive corn-soybean rotation system in the U.S. Midwest faces increasing risks in maintaining high crop yield and environmental sustainability, especially under a changing climate and management intensification. Cover crops have been proposed as a straightforward and promising adaptation to fit in the current system in the U.S. Midwest and increase its resilience and sustainability. However, the current state of cover crop knowledge remains limited and localized, and adoption by farmers has been slow. Quantifying these outcomes across different environments and practices remains a major challenge. This project uses a comprehensive and integrated approach to assess cover crops at commercial farmlands for the central Corn Belt by combining airborne-satellite sensing, agroecosystem modeling, and economic analysis. Data collected from multi-scale airborne-satellite sensing (including airborne hyperspectral sensing and satellite fusion data) and field plant and soil samples will be used to implement and validate a process-based model (Ecosys) at the field level. We will then assess optimal management strategies of growing cover crops in the current corn-soybean rotation system, from three management aspects (cover crop types, planting time, termination time) in current climate conditions and future climate scenarios, for the three major Corn-Belt states (i.e. Illinois, Iowa and Indiana). We will conduct further economic analysis of cover crop adoption, incorporating different scenarios of crop insurance and emerging carbon market payment.

Team members:
Kaiyu Guan, Bin Peng, Sheng Wang

Recommendations on the optimal nitrogen rate can help farmers reduce the fertilizer cost while maintaining the high productivity, thus maximizing net economic returns and reducing nutrient loss. This project aims to maximize the NREC historical and current investment in N rate database development in order to better serve Illinois farmers in nutrient management. This project will compile the most comprehensive NREC N trial database and use the database to upgrade the MRTN calculator. The project will also develop a next generation MRTN calculator based on observation-constrained model simulations. The project will finally develop a web-based platform for both the upgraded and next-generation MRTN calculators to serve Illinois farmers and provide training, outreach, and education to farmers. Check the on-line tool at https://harvest.ncsa.illinois.edu/nitrogen-advisor.

Team members: Andrew Margenot, McKenzie Johnson, Kaiyu Guan, Bin Peng

The U.S. Corn Belt produces about 30% of the world’s corn and soybean, which depends on the use of fertilizers containing both nitrogen (N) and phosphorus (P). However, heavy fertilizing directly affects farmers in terms of expenses and threatens valuable natural resources. Yet, farmers’ perceptions of nutrient management challenges vary widely as does their willingness to adopt novel nutrient management approaches. Thus, an urgent unmet need exists to provide farmers with and help them adopt effective and trusted nutrient management tools that are informed by advanced science and technology as well as by farmer-based practical expertise. This project addresses these needs by developing a smart and connected “Nutrient Management Community” (NuMC) in rural farming communities in the U.S. Corn Belt. Specifically, we will take an integrated approach using farmer-researcher-farmer knowledge transfers to co-produce an NuMC. Our primary goal is to develop science-driven recommendations on N and P management that can be tailored to different farmers’ needs.

Team members: Andrew Margenot, Shengnan Zhou, Bruce L Rhoads, Sheng Wang, Kaiyu Guan

Phosphorus (P) loss through streambank erosion can contribute to up to one-third of total riverine P export (e.g., Iowa). However, the contribution of this overlooked non-point source P losses in Illinois remains unknown. Quantifying streambank erosion –where and how much– is necessary to distinguish non-point source P responsiveness to best management practices, which can be used to identify and guide cost‐efficient mitigation strategies. This project focuses on quantifying non-agricultural sources of P that leach into the Mississippi River, clarifying agricultural P contributions. To achieve this goal, this project uses field data collection, airborne-satellite integrative sensing, and process-based watershed modeling to quantify river bank erosion and P loss. The study will provide much-needed information for a 2025 milestone in the Illinois Nutrient Loss Reduction Strategy to reduce P losses to the Mississippi River and ultimately the Gulf of Mexico.

Team members: Bin Peng, Kaiyu Guan, Sheng Wang, with collaborators from University of Minnesota and Indiana University

Tillage is an essential farming practice that is closely tied to production cost, crop yield, and environmental sustainability. The U.S. Midwest has seen a recent trend of shifting from conventional tillage to more conservative tillage (e.g., no-till), though the adoption rate of no-till is still low (~35% by 2017) and its change is relatively stagnant. However, there is still no consensus in the existing scientific literature about the impacts of changing tillage practices on crop production and environmental sustainability, mainly because of the spatially varying soil, weather, and management conditions.  In this proposal, we aim to innovatively integrate meta-analysis, airborne and satellite data, and process-based modeling to conduct high spatiotemporal assessments of tillage impacts on crop productivity and environmental sustainability (soil carbon sequestration, GHG emissions, and water quality) in the U.S. Midwest. The following four major Midwest states will be included as our study domain: Illinois, Indiana, Iowa, and Minnesota.

Team members: Kaiyu Guan, Wendy Yang, DK Lee, Evan DeLucia, Carl Bernacchi, Bin Peng, Sheng Wang, and partners from University of Minnesota, DOE Berkeley Lab, University of Buffalo

Accurate and rapid field-level quantification of Carbon Intensity (CI) at a regional scale is critical to facilitate adoption of new technologies to increase crop productivity and reduce its carbon footprint. This project will develop a commercial solution, SYMFONI, to accurately and cost-effectively estimate SOC and N2O simultaneously on a per-field basis, with the capability to perform these estimates for a large geographic region. SYMFONI is a “system of systems” solution that deeply integrates airborne-satellite remote sensing, process-based modeling, deep learning, atmospheric inversion, vehicle-based mobile sensing, and high-performance computing. The technology innovation and commercial solution from this project build the foundation for accurate carbon accounting in climate-smart agriculture and scientifically rigorous measurement, reporting, and verification (MRV) in the emerging agricultural carbon market. See more details at https://sustainability.illinois.edu/research/smart-farms-project/

Team members: Kaiyu Guan, Carl Bernacchi, DK Lee, Jong Lee, Evan DeLucia, Bin Peng, Sheng Wang

Developing scalable, accurate and cost-effective approaches to quantify field-level Carbon Intensity (CI) is essential for climate smart agriculture. To achieve this goal, the first step is to establish open-source and high-resolution benchmark datasets to support testing emerging monitoring technologies. However, existing field observation sites/networks lack state-of-the-art equipment and missed the opportunities of using advanced remote sensing data to monitor field-level greenhouse gas emissions and CI. MBC-Lab targets collecting carbon-intensity related variables such as soil organic carbon data, topographical data, soil pH, airborne hyperspectral imagery, satellite Earth observation, and other data from soil sensors, soil chambers, and eddy flux covariance towers. MBC-Lab is the perfect testbed for measuring emissions from biofuel production, and this platform will provide data and technology that can be applied to existing markets and broader production agriculture for ‘smart farming’ and environmental sustainability. See more details at https://sustainability.illinois.edu/research/smart-farms-project/

Team members:
Kaiyu Guan, Bin Peng, Evan DeLucia, and collaborators from NOAA and University of Colorado, JPL, Carnegie.

With rising demands of food and fiber from a growing global population, the agricultural landscape plays an increasingly important role in the global carbon cycle. Cropland also represents one of the biggest opportunities for carbon sequestration. However, there is a lack of comprehensive carbon monitoring systems over cropland. This NASA Carbon Monitoring Systems (CMS) project integrates both bottom-up (inventory and process-based modeling) and top-down (atmospheric inversion) approaches to jointly quantify the carbon budget for the US Corn Belt. Multiple streams of satellite remote sensing data are used to improve carbon budget estimations. This effort carries a great promise to further constrain the regional and global carbon cycle and the carbon budget products generated from this project can be used by the public and private sectors to design effective policies and management practices that can contribute to stabilizing atmospheric CO2 concentrations. See more information at https://carbon.nasa.gov/cgi-bin/inv_pgp.pl?pgid=3748&fullab=1.

Team members: Lisa Ainsworth, Carl Bernacchi, Kaiyu Guan, Bin Peng

Global environmental changes are stressing our crop production system. Deepening our understanding of mechanisms and potential adaptation pathways for the environmental stresses on crop production is critical for global food security. ASC scientists have been striving to quantify and partition different mechanisms of high temperature, VPD and ozone stress impacts on corn and soybean yield from plant to ecosystem scales, combining the field experiments and process-based modeling. The long-term goal of these projects is to incorporate experiment-based evidence of important physiological and biological mechanisms into process-based crop models to assess environmental change impacts and assess possible adaptive traits for crop breeding for crops grown in the U.S. Corn Belt.

Team members:
Wang, Guan, Ainsworth, Schwing, Katz, and collaborators from University of Chicago, Argonne National Lab.

The agriculture industry of Illinois employs around 1.5 million workers, covers 27 million acres (75% of the state), produces more than $19 billion GDP annually, and is an essential pillar for the Illinois economy. However, emerging challenges, such as increased extreme weather, environmental degradation, and increased biotic stress, all threaten the sustainable development of agriculture. This project aims to develop an innovative high-resolution monitoring system to enable agricultural stakeholders to maximize crop yield and minimize negative environmental impact of agriculture. The technology integrates field measurements, airborne hyperspectral imaging, and multi-source satellite fusion data through radiative transfer process-guided machine learning to quantify crop characteristics, farming management practices, and environmental conditions for every field across Illinois and beyond. This monitoring system significantly strengthens stakeholders to detect emerging agricultural threats, mitigate climate change impact, and increase short-term and long-term economic return. See more details at https://dpi.uillinois.edu/advancing-sustainable-agriculture/