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Rhizosphere Observations Optimizing Terrestrial Sequestration

America's vast terrestrial resources (over 520 million hectares of crop, range and forestland) are strategic assets essential for sustainable economic growth. While advances in technology have resulted in a ten-fold increase in crop productivity over the past hundred years, soil quality has declined, incurring a soil carbon debt equivalent to 65 parts per million (ppm) of atmospheric carbon dioxide (CO2). The soil carbon debt also increases the need for costly nitrogen fertilizer, which has become the primary source of nitrous oxide (N2O) emissions, a greenhouse gas. The soil carbon debt also impacts crop water use, increasing susceptibility to drought stress, which threatens future productivity. Given the scale of domestic (and global) agriculture resources, there is tremendous potential to reverse these trends by harnessing the photosynthetic bridge between atmospheric carbon, plants, microbes and soil. Development of new root-focused plant cultivars could dramatically and economically reduce atmospheric CO2 concentrations while improving productivity, resilience and sustainability. To this end, projects in the ARPA-E Rhizosphere Observations Optimizing Terrestrial Sequestration (ROOTS) program seek to develop advanced technologies and crop cultivars that enable a 50 percent increase in soil carbon accumulation while reducing N2O emissions by 50 percent and increasing water productivity by 25 percent.

For a detailed technical overview about this program, please click here.  


Colorado State University

Root Genetics in the Field to Understand Drought Adaptation and Carbon Sequestration

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.

Iowa State University

High-throughput, High-resolution Phenotyping of Nitrogen Use Efficiency Using Coupled In-plant and In-soil Sensors

Iowa State University will develop new sensors that measure the amount of nitrogen in soils and plants multiple times per day throughout the growing season. Nitrogen fertilizer is the largest energy input to U.S. corn production. However, its use is inefficient due to a lack of low-cost, effective nitrogen sensors. Year-to-year variation in nitrogen mineralization, due to differences in soil water and temperature, creates tremendous uncertainty about the proper fertilizer input and can cause farmers to over-apply. As a result, nitrogen fertilizer is lost from croplands to the surrounding environment where it pollutes air and water resources. To address this problem, the team will develop a novel silicon microneedle in-plant nitrogen sensor and a microfluidic soil nitrogen sensor. The microscale needles can be inserted into multiple sites of the plant to provide frequent and accurate monitoring of nitrate uptake, and for the first time provide a view of plant nitrogen use as the plant and roots develop. The team will also develop an automated microfluidic sensor which will measure the amount of nitrate in soil by extracting very small amounts of solution from the soil. The microfludic technology on which soil sensors are based can be produced at low cost. The combination of these two sensors will allow for a deeper understanding of plant nitrogen use and how it correlates with nitrate levels in the soil. These new sensors will accelerate the effort to identify, select, and breed new crops with improved nitrogen use efficiency. And the project will help increase the energy efficiency of our agriculture systems while reducing input costs, greenhouse gas emissions, and nitrate pollution of aquatic ecosystems.

Lawrence Berkeley National Laboratory

Associated Particle Imaging (API) for Non-invasive Determination of Carbon Distribution in Soil

Lawrence Berkeley National Laboratory will develop a field-deployable instrument that can measure the distribution of carbon in soil using neutron scattering techniques. The system will use the Associated Particle Imaging (API) technique to determine the three-dimensional carbon distribution with a spatial resolution on the order of several centimeters. A compact, portable neutron generator emits neutrons that excite carbon and other nuclei. The excited carbon isotopes emit gamma rays that can be detected above the ground with spectroscopic detectors and used as a proxy to estimate the amount of carbon in the soil. Neutron exposure at the applied rates from the instrument will not damage plants or affect their growth rates, and protocols for safe operation of the system will be developed in consultation with radiation health personnel. The advantage of API is that it can spatially map the carbon distribution in soil more accurately than other imaging methods that heavily favor the top layers of soil. The spatial resolution of API will allow the measurement of changes in carbon fraction related to depth and changes associated with plant root architecture and soil porosity. Since repeated measurements are possible over the growing season, the API system will provide a bridge to understanding soil carbon sequestration. If successful, API data will enable the optimization of soil management practices as well as the opportunity to optimize plants for specific traits, such as larger root mass, and deeper roots.

Lawrence Berkeley National Laboratory

Integrated Imaging and Modeling Toolbox for Accelerated Development of Root-focused Crops at Field Scales

Lawrence Berkeley National Laboratory (LBNL) will develop an imaging-modeling toolbox to aid in the development of more efficient crops at field scales. The approach is based on a root phenotyping method called Tomographic Electrical Rhizosphere Imaging (TERI). TERI works by applying a small electrical signal to a plant, then measuring the impedance responses through the roots and correlating those responses to root and soil properties. Key target traits of the LBNL project include root mass, root surface area, rooting depth, root distribution in soil, and soil moisture content and texture. The TERI technology will be sensitive enough to distinguish between various plant varieties. The process is minimally invasive, and by doing repeated TERI measurements over the growing season, critical root architectural traits and their dynamic changes over time can be quantified for a range of soil conditions. From laboratory studies, LBNL and its partners will integrate hardware and software tools to develop a field deployable instrument based on the TERI technology. LBNL is partnered with the Noble Foundation to apply the TERI technology to wheat breeding and identify wheat varieties with improved root characteristics, and also link visible above-ground phenotypes with the desired root characteristics. The team will utilize the TERI technology to characterize plants in both controlled laboratory and field studies, and use the data generated to improve ecological models predicting plant performance in the environment.

Pennsylvania State University

DEEPER: An Integrated Phenotyping Platform for Deeper Roots 

Pennsylvania State University (PSU) will develop DEEPER, a platform for identifying the traits of deeper-rooted crops that integrates breakthroughs in nondestructive field phenotyping of rooting depth, root modeling, high-throughput 3D imaging of root architecture and anatomy, gene discovery, and genomic selection modeling. The platform will be deployed to observe maize (corn) in the field under drought, nitrogen stress, and non-stressed conditions. Their key sensor innovation is to measure leaf elemental composition with x-ray fluorescence, and use it as a proxy for rooting depth. This above-ground, high throughput measurement for root depth will enable plant breeders to screen large populations and develop deep rooted commercial varieties. The team will also develop an automated imaging system for excavated roots that, with associated computer vision software, will identify architectural traits of roots. Lastly, they will greatly enhance a laser-based imaging platform to determine root anatomy. The combination of these technology platforms with advanced computational models developed for this program will allow PSU to determine the depth of plant roots, enabling better quantification of root biomass. As a full system platform, they aim to enable the breeding of maize with deeper roots that sequester more carbon and are more efficient in their utilization of nitrogen and water. The team will also contribute data to a nationwide dataset that seeks to study the interactions between genes and the environment. The dataset will include extensive plant data across multiple environments, a breeding toolkit of major genes regulating root depth, and genomic selection models for root depth, drought tolerance, and nitrogen use efficiency.

Sandia National Laboratory

Multi-Modal Monitoring of Plant Roots for Drought & Heat Tolerance in the US Southwest

Sandia National Laboratory will develop novel, field-deployable sensor technologies for monitoring soil, root, and plant systems. First, the team will develop microneedles similar and shape and function to hypodermic needles used in transdermal drug delivery and wearable sensors. The minimally invasive needles will be used to report on sugar concentrations and water stress in leaves, stems, and large roots in real-time. Continuously monitoring the sugar concentrations at multiple locations will be transformative in understanding whole plant carbon dynamics and the function of the vascular tissues that conduct sugars and other metabolic products downward from the leaves. The second key technology are gas chromatographs deployed in the soil and near plants in order to monitor volatile organic compounds (VOC). Plants synthesize and release volatile organic compounds both aboveground and belowground that act as chemical signals or in response to biotic stress (damage from insects, bacteria, etc.) or abiotic stress (such as drought, flooding, and extreme temperatures). VOCs modulate biomass uptake and the team hopes to better understand soil composition by measuring VOC transport. The team's integrated microsensor technologies will be deployed in arid environments in both natural and agricultural lands to characterize whole plant function in both environments. Applying these sensors to plants in arid environments could assist in re-greening arid ecosystems with new specially bred plants developed and selected to improve soil function with less water and nutrient requirements while depositing more soil carbon.

Stanford University

Thermoacoustic Root Imaging, Biomass Analysis, and Characterization

Stanford University will develop a non-contact root imaging system that uses a hybrid of microwave excitation and ultrasound detection. Microwave excitation from the surface can penetrate the soil to the roots, and results in minor heating of the roots and soil at varying levels depending on their physical properties. This heating creates a thermoacoustic signal in the ultrasound domain that travels back out of the soil. The team's advanced ultrasound detector has the ability to detect these signals and maintain sufficient signal-to-noise ratio for imaging and root biomass analysis. The team will develop a suite of image processing algorithms to convert the data into an understanding of root properties including structure, biomass density, and depth. Plant physiologists from the Carnegie Institution for Science will partner with Stanford to characterize maize roots under various drought conditions as well as soil type and density variations. Since the entire system is non-contact, it eliminates the need to make good physical contact with the irregular soil surfaces. Over a three-year period, the team will first demonstrate the feasibility of non-contact thermoacoustics for root imaging under laboratory conditions, then develop and test a thermoacoustic system in the field. If successful, Stanford's system could examine root structures in a noninvasive manner that produces images far more advanced than current imaging methods.

Texas A&M University

A Field-Deployable Magnetic Resonance Imaging Rhizotron for Modeling and Enhancing Root Growth and Biogeochemical Function

Texas A&M AgriLife Research will develop low field magnetic resonance imaging (LF-MRI) instrumentation that can image intact soil-root systems. The system will measure root biomass, architecture, 3D mass distribution, and growth rate, and could be used for selection of ideal plant characteristics based on these root metrics. It will also have the ability to three-dimensionally image soil water content, a key property that drives root growth and exploration. Operating much like a MRI used in a medical setting, the system can function in the field without damaging plants, unlike traditional methods such as trenching, soil coring, and root excavation. The team will test two different approaches: an in-ground system shaped like a cylinder that can be inserted into the soil to surround the roots; and a coil device that can be deployed on the soil surface around the plant stem. If successful, these systems can help scientists better understand the root-water-soil interactions that drive processes such as nutrient uptake by crops, water use, and carbon management. This new information is crucial for the development of plants optimized for carbon sequestration without sacrificing economic yield. The project also aims to help develop ideal energy sorghum possessing high root growth rates, roots with more vertical angles, and roots that are more drought resistant and proliferate under water limiting conditions.

UHV Technologies, Inc.

Low Cost X-Ray CT System for in-situ Imaging of Roots

UHV Technologies will develop and demonstrate a low cost, field deployable 3D x-ray computed tomography system that will image total root systems in the field with micron-size resolution and can sample hundreds of plants per cycle. This system is based on UHV's low cost linear x-ray tube technology and sophisticated reconstruction and image segmentation algorithms. The linear x-ray tube technology was originally designed for extremely high throughput scrap aluminum sorting, and when used with an array x-ray detector the system can also produce 2D and 3D imaging of plant roots in the field without the use of heavy, moving gantry systems normally used for trait observation. Maize (corn) was chosen as the crop to study due to its robust root system, well-characterized genetic resources, sequenced genome, and access to existing breeding pipelines with commercial potential. The system will be tested in two environments, at the University of Wisconsin with clay-like soil and at Texas A&M University which features sandy soil. Due to its small size, high resolution and fast imaging of fine roots, low power consumption, large penetration depth (i.e. the ability to see through several feet of soil) and ease of use in the field, the proposed system will increase the speed and efficacy of discovery and deployment of improved crops and systems. These advanced crops can improve soil carbon accumulation and storage, decrease nitrogen oxide emissions, and improve water efficiency. If successful, this new level of imaging will be invaluable to scientists seeking to understand how environmental conditions and plant trait variations contribute to carbon deposition through root development.

University of Florida

Rays for Roots - Integrating Backscatter X-Ray Phenotyping, Modelling, and Genetics to Increase Carbon Sequestration

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.

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