Award Abstract # 2345039
NSF Convergence Accelerator Track J Phase 2: CropSmart - a digital twin for making wiser cropping decisions nationwide

NSF Org: ITE
Innovation and Technology Ecosystems
Recipient: GEORGE MASON UNIVERSITY
Initial Amendment Date: December 15, 2023
Latest Amendment Date: December 10, 2024
Award Number: 2345039
Award Instrument: Cooperative Agreement
Program Manager: Michael Reksulak
mreksula@nsf.gov
 (703)292-8326
ITE
 Innovation and Technology Ecosystems
TIP
 Directorate for Technology, Innovation, and Partnerships
Start Date: December 15, 2023
End Date: November 30, 2026 (Estimated)
Total Intended Award Amount: $5,000,000.00
Total Awarded Amount to Date: $4,006,642.00
Funds Obligated to Date: FY 2024 = $2,003,012.00
FY 2025 = $2,003,630.00
History of Investigator:
  • Liping Di (Principal Investigator)
    ldi@gmu.edu
  • Qian Du (Co-Principal Investigator)
  • Juan Sesmero (Co-Principal Investigator)
  • Haishun Yang (Co-Principal Investigator)
  • Cenlin He (Co-Principal Investigator)
  • Fei Chen (Former Co-Principal Investigator)
Recipient Sponsored Research Office: George Mason University
4400 UNIVERSITY DR
FAIRFAX
VA  US  22030-4422
(703)993-2295
Sponsor Congressional District: 11
Primary Place of Performance: George Mason University
4400 UNIVERSITY DR
FAIRFAX
VA  US  22030-4422
Primary Place of Performance
Congressional District:
11
Unique Entity Identifier (UEI): EADLFP7Z72E5
Parent UEI: H4NRWLFCDF43
NSF Program(s): Convergence Accelerator Resrch
Primary Program Source: 01002425DB NSF RESEARCH & RELATED ACTIVIT
01002627DB NSF RESEARCH & RELATED ACTIVIT

01002526DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 131Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.084

ABSTRACT

Healthy crop production in the U.S. is critical for not only the food and nutrition security of the U.S. and the world but also the prosperity of the U.S. economy. The USDA Agricultural Innovation Agenda calls for increasing U.S. agricultural production by 40% while cutting its environmental footprint in half by 2050. Sound crop management decision-making is a key to achieving this ambitious goal. An example of such decision-making is ?should I irrigate my cornfield today? If so, by how many inches of water?? Traditionally, such decisions are made by individuals based on their empirical judgment, which is often subjective and less optimal. Science-based, data-driven approaches for cropping decision-making rely on timely and accurate information on current and predicted future conditions of crop and environment to make optimal decisions. However, it remains a challenge for stakeholders to adopt the data-driven approach because they do not have full and effective access to the timely and accurate information and lack facilities or knowledge to process the information. This project will meet the challenge by offering the data-driven optimal cropping decision-making services nationwide up to field scales through developing and operating the CropSmart digital twin. The services will be accessible to users through both web portals and smartphone Apps. This project will help USDA to archive its innovation goal, enhance food and nutrition security of the U.S. and the world, and bring hundred-million-dollar economic return and huge environmental benefits to U.S. economy and society annually.

CropSmart, to be built and operated by this project, is a digital replica of real-world cropping systems over the contiguous US up to 10-m spatial resolution. It will not only accurately represent the current crop and environment conditions, but also predict, with acceptable confidence levels, future conditions with hypothetical ?what if? scenarios, resulting in actionable predictions. CropSmart will provide three services to users: 1) user-specific decision ready information on which the user can make data-driven decision; 2) ?what if? tradeoff service which will generate consequences (e.g., yield, economic return, or environmental footprint) of different user decision options so that the user can find the optimal decision; and 3) decision advice service which will automatically generate optimal decision based on a user?s decision goal. CropSmart will be built by integrating the advanced remote sensing, crop and environmental modeling, AI/ML, agro-geoinformatics, and digital twin technologies through the multi-disciplinary convergence approach. The major project activities will include: 1) implementing CropSmart to support at least 6 types of top-priority decision-making use-cases specified by the user community; (2) deploying CropSmart operationally to cultivate its user community and show its gaming-change impacts; 3) broadening adoption, participation, and impact through a comprehensive education, extension, and outreach program; and (4) establishing a community-based CropSmart.org and implement the sustainability plan to sustain CropSmart activities after project expires and maximize the long-term project impacts. At the end of the performance period, this project will deliver the CropSmart software package, the operational CropSmart services, and a sustained community of at least 6,000 users.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Liu, Ziao and Di, Liping and Zhang, Chen and Guo, Liying and Shao, Bosen and Li, Hui "The Importance of Early-Season Weather Factors and Vegetation Indices on Prediction of Corn Yield at County Level" International Conference on AgroGeoinformatics , 2024 https://doi.org/10.1109/Agro-Geoinformatics262780.2024.10661099 Citation Details
Shao, Bosen and Di, Liping and Li, Hui and Zhang, Chen and Liu, Ziao and Guo, Liying "Generation of NAIP-like earth surface imagery using Generative Adversarial Networks" International Conference on AgroGeoinformatics , 2024 https://doi.org/10.1109/Agro-Geoinformatics262780.2024.10660730 Citation Details
Zhao, Haidong and Zhang, Lina and Wan, Nenghan and Avenson, Tom J and Welch, Stephen M and Lin, Xiaomao "Sensitivity changes of US maize yields to extreme heat through timely precipitation patterns" Environmental Research Communications , v.6 , 2024 https://doi.org/10.1088/2515-7620/ad6404 Citation Details

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