Performing Department
(N/A)
Non Technical Summary
Feedstock supply chain operations exhibit critical gaps, including biomass feedstock cropping systems that are sensitive to both economic and ecological functions, feedstock adaptability to different climatic and edaphic conditions, as well as feedstock crop resistance to abiotic and biotic stresses. Matching feedstocks with the appropriate cropping system and production region will maximize productivity per unit of land used, optimize production planning, and assure consistency in feedstock quality. Also uncertain are the environmental impacts of large-scale feedstock production on natural resources, such as soil quality, water quantity and quality, soil carbon dynamics and sequestration potential, and biodiversity. Thus, we envision an interdisciplinary research approach that not only identifies biomass crops and associated germplasm but also proposes a sustainable biomass cropping system that can withstand marginal growing conditions and promote ecosystem services. This multi-year, multi-location project will use a combination of feedstock germplasm and field production investigations to develop a high throughput phenotyping methodology to identify the best performing bioproducts crops as feedstocks for renewable energy and bio-based products while maximizing carbon capture and retention in the soil. The results from this research will be used to train graduate/undergraduate students and develop learning modules to train next generation bioeconomy workforce. The objectives and expected outcomes of this project are well-aligned with the USDA-NIFA-AFRI: Bioenergy, Natural Resources, and Environment program area priority of Sustainable Bioeconomy through Biobased Products by developing a scalable biomass production system resilient to drought and low input conditions, capable to sequester carbon, and provide ecosystem benefits. This is a new investigator standard grant application.
Animal Health Component
0%
Research Effort Categories
Basic
30%
Applied
40%
Developmental
30%
Goals / Objectives
The overarching goal of this proposed project is to develop a feedstock-based biomass production system for the Southeast region of the U.S. with improved carbon and water footprint for the generation of bio-based products. The specific objectives of this proposed project are to: Develop remote sensing-based phenotyping techniques to assess drought tolerance rapidly and non-destructively in energy cane, switchgrass (Panicum virgatum), and perennial sorghum (Sorghum bicolor x S. propinquum hybrid)Screen elite energy cane genotypes for water use efficiency and nutrient use using tools developed in Objective 1 and further assess the most water use efficient entries as a biomass system that integrates legume intercropping.Measure environmental impacts of the monoculture feedstock biomass system compared to the legume integrated biomass system using a life cycle analysis of carbon and water footprint.Train next-generation bio-economy workforce (students and farmers) through an integrated education curriculum.
Project Methods
In this project, we plan to conduct field experiments at two sites: Weslaco and Corpus Christi, at Texas A&M AgriLife Research and Extension Centers.Drought Tolerance Experiment: Elite genotypes from the Texas A&M AgriLife energy cane germplasm improvement program of CO-PD Dr. Jorge DaSilva will be characterized for yield potentials, and quality characteristics (compositional profiles) as affected by genotype and growing environment. In year 1, six energy cane genotypeswill be grown with switchgrass and seeded perennial sorghum (n = 5). Three irrigation levels will be used based on evapotranspiration (ET) needs (100% ET, 50% ET, and rainfed). The experimental design will be a completely randomized split-plot design in four replications.Best-performing energy cane and forage genotypes (in terms of yield, quality, stress tolerance, and water and nutrient efficiency) will be identified for biomass yield under low input conditions.Biomass System with Legume Intercropping: The three most drought-tolerant warm-season grass genotypes from the Drought Tolerance Experiment will be planted at the Corpus Christi location in year 2. The experimental design will be a completely randomized block design with split plots where the main plots are irrigation to allow for drip irrigation measured by the flow meter. Sub-plots will be a factorial arrangement of biomass grasses and legume intercropping in triplicate, for a total of 81 plots (4 rows × 12 m; 3 irrigation levels × 3 grasses × 3 legumes × triplicate replicates). The irrigation treatments will be the same as for the Drought Tolerance Experiment, which is 100% ET, 50% ET, and rainfed.Objective 1: Develop remote sensing-based phenotyping techniques to assess drought tolerance of biomass systems:Ground-based measurements of crop water status: At both sites, soil samples will be collected before planting and after harvest to obtain volumetric water content. Additionally, we will use the GoField Plus system (Goanna Ag Pty. Ltd., QLD Australia) to measure crop water status in each treatment plot. This system includes a 1-m deep capacitance probe providing soil moisture and temperature status in 10 cm increments along with continuous canopy temperature via infrared thermometry.Unmanned Aerial System (UAS) data collection: UAS equipped with Red Green Blue (RGB), multi-spectral (MS), and thermal sensors and Real TimeKinematic Ground Positioning System (RTK-GPS) module will be flown weekly from planting to harvest to collect high spatiotemporal remote sensing data. Collected UAS data will be processed using an image processing pipeline developed by our program to generate canopy features, vegetation indices, and canopy temperature measurements.Establish the relationship between UAS-based phenotypic parameters and crop water stress measurements: We intend to utilize the canopy feature measurements obtained from RGB and multi-spectral sensors to understand the impact of water stress on crop growth and development. Additionally, the features will be correlated with measurements obtained from GoField Plus system on soil water status. Based on this understanding and assessment, we hope to identify the UAS-based features that will most likely detect early water stress and be used to quantify the magnitude of stress. Apart from the features based on RGB and multi-spectral sensors, we will evaluate the utility of UAS-based CT measurements for the early detection of water stress.Develop biomass yield prediction model using phenotypic measurements obtained from UAS:We hypothesize that yield prediction models may be further refined if multi-temporal crop phenotypic information derived from UAS data is coupled with non-linear machine learning algorithms. In recent years, an advanced machine learning technique called deep learning has emerged as an alternative approach to solving complex problems, which will be exploited to estimate biomass yield from the UAS data in this project.Objective 2: Screen elite energy cane genotypes for water use efficiency and nutrient use using tools developed in Objective 1 and further assess the most water use efficient entries as a biomass system that integrates legume intercropping:By integrating UAS data along with ground measurements and data obtained from GoField Plus system, we hope to develop and utilize the screening tools developed in objective 1 and identify drought-tolerant energy cane lines. Objective 3: Measure the environmental impacts of the monoculture feedstock biomass system and the legume integrated biomass system using a life cycle analysis of carbon and water footprint:Two major tasks will be completed under this objective.Task 3.1: Create the life cycle inventory database for the two biomass product systems: The flowcharts of the two biomass product systems will be developed first, which will clearly show all the processes involved in the three stages defined as the boundaries of the proposed LCA studies. The flowcharts will also include the input and output information of each process. Then, a life cycle inventory database will be created based on data collected from this project and the existing database, which will include quantitative data on each process in the flowcharts.Task 3.2: Assess the environmental impacts of the two biomass product systems and provide necessary interpretation for stakeholders: Different environmental impacts will be categorized first for comparative analysis. The environmental impacts will also be normalized to compare with other biomass products.Objective 4: Train the next-generation bioeconomy workforce (students and farmers) through an integrated education curriculum:Task 4.1: Develop research-based learning modules for different levels of students: The proposed project will create different types of curricular materials with biomass/bioenergy-relevant content, including semester-long course projects with research components, short-term course projects (1-2 weeks), individual learning modules to be used in classroom lectures, extra-credit learning modules to be used outside class time.The topics of enhanced curricular materials will include but not be limited to remote sensing, UAS, biomass, and LCA. Research components and concepts will be extracted from the proposed research activities and embedded into these curricular materials. Teaching instructions and video demonstrations will be provided for all enhanced curricular materials for easy adoption and dissemination.Task 4.2: Increase farmer's and students' awareness and knowledge of biomass and bioenergy by implementing learning modules in classrooms and webinars: The developed curricular materials and instructions will be shared with high school and community college teachers through existing connections established in several federally funded STEM education projects including NASA MUREP INCLUDES NSF HSI (award 1928611, Dr. Li is Co-PI), and NSF RET. The project team will also help the teachers to implement the curricular materials by visiting their classrooms. The webinars will be promoted to the students and farmers through NASA MUREP INCLUDES and USDA REAP projects (Award 50-037-701289094, Dr. Li is PI).Task 4.3: Provide research training to high school and undergraduate students. The funded undergraduate students under the proposed project will be invited to join the USDA REEU program during the summer to experience an in-depth research experience focusing on experimentation, simulation, and modeling in energy and the environment. The research activities proposed in this project will be converted into mini research projects to be used in a USDA HSI project (award 2022-77040-37631, Dr. Li is PI) to train high school, undergraduate, and graduate students through a team-based research program.