NSF Convergence Accelerator Track J: Building a digital twin for national-scale field-level crop monitoring, prediction, and decision support

Crop production in the U.S. feeds not only the U.S. but also the world. During the 2020/2021 fiscal year, U.S. exports accounted for over 25% of total grain traded globally. A healthy crop cropping systems (CCS) is vital for achieving food and nutrient security of the U.S. and the world and enhancing the competitiveness of U.S. agriculture in the world market.

Yet, crop production creates large environmental footprint. 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 in reaching this ambitious goal.

Traditionally, crop management decisions are made by individuals based on their empirical judgment, which is often subjective and far from optimal. On the other hand, the science-based, data-driven approach for crop management decision-making relies on timely and accurate information on current and predicted future conditions of crop, soil, weather, and market to make optimal decisions.

Studies demonstrated that the data-driven approach can overcome the inherent deficiencies in the empirical approach and bring significant economic and environmental benefits. However, it remains a challenge for stakeholders to utilize the data-driven approach because they don’t have full and effective access to the timely and accurate information and lack facilities or knowledge to process the information.

This project will provide such timely information and decision support to stakeholders for enabling the data-driven optimal decision-making nationwide at field scales by developing the CropSmart Digital Twin (CSDT). CSDT will not only accurately represents the current conditions, but also predict, with acceptable confidence levels, future conditions of CCS with hypothetical “what if” scenarios, resulting in actionable predictions.

The project will provide significant help in reaching the USDA Innovation goal and greatly enhance food and nutrition security of the U.S. and the world.

Crop production is the foundation for food and nutrition security in the U.S. and the world. However, it also creates large environmental footprint. The USDA Agricultural Innovation Agenda calls for increasing U.S. agricultural production by 40% while cutting its environmental footprint in half by 2050.

The data-driven approach for crop management decision-making, which relies on timely and accurate information on current and predicted future conditions of crop, soil, weather, and market to make optimal management decisions, has demonstrated its great potential to help USDA reach its ambitious goal. However, it remains a challenge for stakeholders to adopt the approach because they don’t have effective access to the decision-ready information (DRI) and lack facilities or knowledge to process the information.

This project proposes to build the CropSmart Digital Twin (CSDT) with innovative Earth system DT technologies to facilitate the data-driven approach. The overarching goal is to ensure food and nutrition security by enhancing crop productivity and reducing environmental footprint in the U.S. through wide adoption of the data-driven approach enabled by CSDT.

The major project activities include: (1) understanding stakeholders’ requirements on DRI and decision support; (2) identifying existing data, technologies, and gaps for CSDT; (3) quickly prototyping CSDT by integrating existing technologies and developing gap-filling technologies; 4) broadening participation and impact by training agricultural workforce through comprehensive extension; and (5) establishing a community-based CSDT network for long-term sustainability. This project explores the convergent approach for quickly constructing an operational DT through integration of multi-disciplinary components and services with interoperability technology.

It demonstrates the advantage of multi-disciplinary collaboration and feasibility, usability, and value of DT as a multi-disciplinary integration platform for enabling the data-driven approach. The project will help USDA reach its Innovation Agenda goal and enhance food and nutrition security of the U.S. and the world.

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.