Crop production is the foundation for food and nutrition security in the U.S. and world. However, it also creates large climate and environmental footprint. The USDA Agricultural Innovation Agenda calls for increasing U.S. agricultural production by 40% while cutting the climate and environmental footprint of U.S. agriculture in half by 2050. Sound crop management decisions are the key for reaching this ambitious goal. Examples of such decisions are “when should I irrigate my corn field with how much water?”, and “when should I fertilize my wheat field?”.  The science-based, data-driven crop management decision making approach, which relies on timely and reliable information on current and predicted future conditions of crop, soil, weather, and market conditions to make optimal management decisions, can achieve significant economic and environmental benefits and cost savings. However, it remains a great challenge for decision makers to adopt the approach because they don’t have effective access to the timely and accurate information in a format that provides specific support for actions and decisions and lack facilities and/or knowledge to handle the information. 

The recent innovation in Earth Science Digital Twin (ESDT) provides us a great opportunity to facilitate the data-driven approach in crop management decision making. This project proposed to build, operate, and promote an ESDT, the CropSmart Digital Twin (CSDT), to facilitate the data-driven cropping decision making nationwide. The overarch goal is to ensure the food and nutrition security of the U.S. and the world by enhancing the crop productivity and reducing environmental footprints in the U.S. through wide adoption of the data-driven approach enabled by CSDT. The major goal of the phase 1 of this project, of which this project outcome report covers, is to demonstrate the feasibility and potentials of proposed CSDT to facilitate the data-driven cropping decision making. To realize Phase 1 goal, the project team has conducted the following major activities, including: 

(1)  conducting end-user interviews to understand stakeholders’ difficulties, needs, and requirements on cropping decision making. The project team interviewed 19 end users, including farmers, crop advisors, agri-business operators, researchers, extension experts, museum content providers, and state and federal agricultural officers, with 21 generic questions relevant to all end-users and 6 sets of end-user specific questions. In addition, the project team conducted two workshops with a total of 60 participants to solicit requirements and inputs

(2)  designing a preliminary CSDT architecture by identifying components, interfaces, services, data sources, existing technologies, and technology gaps for CSDT to functionally meet stakeholders’ requirements 

(3)  developing the low-fidelity CSDT prototype. After initial user interview, the CropSmart low-fidelity prototype was quickly implemented for corn and soybeans in Nebraska at up to field level (up to 10 meters) with a limited number of cropping parameters and decision-making scenarios by integrating and reusing components and services of existing operational systems to demonstrate the feasibility and potentials of proposed CSDT to meet stakeholders’ decision requirements

(4)  participating in the NSF Convergence Accelerator Curriculum to learn the use-inspired research and team-science principles. The project team drafted the team science collaboration agreement, the intellectual property management plan, and project sustainability plan and created project logo, tagline, short project description, market video, and initial, mid-term, and final project pitches. These materials will be finalized at beginning of Phase-2 and used to guide the Phase 2 implementation  

(5)  preparing the phase 2 proposal for securing NSF funding to develop, operate, and promote operational CSDT.

(6)  providing training and learning opportunities to the next-generation agricultural workforce through participating in this cutting-edge research

(7)  conducting comprehensive outreach and extension activities to broaden community awareness of digital innovation in agriculture

Through those activities, the phase-1 project has archived the following key outcomes:

(1)    Confirmed that there are strong and urgent demands within the end-user community for data-driven cropping decision-making tools 

(2)    Proved that the proposed convergent digital twin solution is technically feasible, applicable, and scalable to meet end-users’ demands on data-driven cropping decision making 

(3)    Learned and successfully practiced the team science and use-inspired convergence research principles

(4)    The phase-2 implementation proposal has been selected by NSF for funding

(5)    Explored the potentials of the convergent approach for quick construction of an operational digital twin through integration of multi-disciplinary components and services with interoperability technology 

(6)    Demonstrated the advantage of multi-disciplinary collaboration and the feasibility, usability, and value of DT as a multi-disciplinary technology integration platform for enabling data-driven crop decision making 

(7)    Provided training and learning opportunities to the next-generation agricultural workforce

(8)    Demonstrated the potentials of CropSmart DT for significantly increasing the capabilities of agriculture science, helping USDA to realize its 2050 Innovation Agenda, and enhancing the food and nutrition security of the US and the world

(9)    Published 3 peer-reviewed papers and a number of conference presentations. 

Last Modified: 04/23/2024
Modified by: Liping Di