Colorado State University (CSU)

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.

Colorado State University (CSU) will develop a high-throughput ground-based robotic platform that will characterize a plant’s root system and the surrounding soil chemistry to better understand how plants cycle carbon and nitrogen in soil. CSU’s robotic platform will use a suite of sensor technologies to investigate crop genetic-environment interaction and generate data to improve models of chemical cycling of soil carbon and nitrogen in agricultural environments. The platform will collect information on root structure and depth, and deploy a novel spectroscopic technology to quantify levels of carbon and other key elements in the soil. The technology proposed by the Colorado State team aims to speed the application of genetic and genomic tools for the discovery and deployment of root traits that control plant growth and soil carbon cycling. Crops will be studied at two field sites in Colorado and Arizona with diverse advantages and challenges to crop productivity, and the data collected will be used to develop a sophisticated carbon flux model. The sensing platform will allow characterization of the root systems in the ground and lead to improved quantification of soil health. The collected data will be managed and analyzed through the CyVerse "big data" computational analytics platform, enabling public access to data connecting aboveground plant traits with belowground soil carbon accumulation.