Plants capture atmospheric carbon dioxide (CO2) using photosynthesis, and transfer the carbon to the soil through their roots. Soil organic matter, which is primarily composed of carbon, is a key determinant of soil’s overall quality. Even though crop productivity has increased significantly over the past century, soil quality and levels of topsoil have declined during this period. Low levels of soil organic matter affect a plant’s productivity, leading to increased fertilizer and water use. Automated tools and methods to accelerate the process of measuring root and soil characteristics and the creation of advanced algorithms for analyzing data can accelerate the development of field crops with deeper and more extensive root systems. Crops with these root systems could increase the amount of carbon stored in soils, leading to improved soil structure, fertilizer use efficiency, water productivity, and crop yield, as well as reduced topsoil erosion. If deployed at scale, these improved crops could passively sequester significant quantities of CO2 from the atmosphere that otherwise cannot be economically captured.
Project Innovation + Advantages:
The University of Florida will develop a backscatter X-ray platform to non-destructively image roots in field conditions. The team will focus their efforts on switchgrass, a promising biofuel feedstock with deep and extensive root systems. Switchgrass is also a good candidate to study because it is a perennial grass with great genetic diversity that is broadly adapted to the full range of environments found in the U.S. The project will leverage a DOE-funded switchgrass common garden with ten identical plantings that span growth zones from Texas to South Dakota. X-ray backscatter systems use a targeted beam to illuminate the part of the plant under observation, and sensors detect the x-rays reflected back to construct an image. The system will not require trenches or other modifications to the field, and will be able to provide three-dimensional root and soil imaging. Software developed by the team will help refine the raw data collected. Image processing and machine learning algorithms will improve image formation and autonomously analyze and extract key root and soil characteristics. In particular, root-vs-soil segmentation algorithms will be developed to identify roots in the imagery and extract geometric-based features such as root length and root diameter. Statistical machine learning algorithms will also be developed and trained to extract information from the imagery beyond the geometric-based features traditionally identified. The project aims to identify the genetic and environmental factors associated with desirable root characteristic that can lead to increased carbon flow and deposition into the soil. If the team is successful, these tools will be broadly applicable to other crops and application areas beyond switchgrass.
If successful, developments made under the ROOTS program will produce crops that will greatly increase carbon uptake in soil, helping to remove CO2 from the atmosphere, decrease nitrous oxide (N2O) emissions, and improve agricultural productivity.
America’s soils are a strategic asset critical to national food and energy security. Improving the quality of soil in America’s cropland will enable increased and more efficient production of feedstocks for food, feed, and fuel.
Increased organic matter in soil will help reduce fertilizer use, increase water productivity, reduce emissions of nitrous oxide, and passively sequester carbon dioxide from the atmosphere.
Healthy soil is foundational to the American economy and global trade. Increasing crop productivity will make American farmers more competitive and contribute to U.S. leadership in an emerging bio-economy.