Water quantity and quality are crucial for agroecosystems as they impact crop productivity and are also indicators of agriculture environmental outcomes. Appropriate water availability ensures adequate moisture, nutrient absorption, and photosynthesis in crops, enabling optimal growth and yield. Meanwhile, agriculture production influences water quantity and quality in the surrounding ecosystems. Poor water quality with high levels of nutrients from agroecosystems leads to downstream eutrophication. Therefore, accurate monitoring and modeling of the whole hydrologic processes (e.g., water use, irrigation, and water quality) are important in ensuring the long-term viability and resilience of agroecosystems.
Core Faculty and Researchers
Bin Peng, Kaiyu Guan, Andrew Margenot, Sheng Wang
Representative Projects
Nutrient loss reduction strategy and quantification for the Mississippi River Basin (funded by NGRREC)
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.
Developing cyber-physical systems for smart irrigation for the US Midwest crops (funded by USDA NIFA)
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.
A missing piece of the Illinois phosphorus puzzle: quantifying statewide streambank erosion to inform effective nutrient loss reduction strategy (funded by NREC)
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.