Progress 03/01/23 to 02/29/24
Outputs
Target Audience:
Nothing Reported
Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?PhD student training: 1. Benjamin Ghansah started his PhD in Computer and Geospatial Science inthe Fall of 2023 at Texas A&M University-Corpus Christi. How have the results been disseminated to communities of interest?Conference presentation Joaquin Haces-Garcia, Hua Li, Jorge da Silva, Jaime Foster, Mahendra Bhandari, Well-to-Tank Life Cycle Assessment of Various Energy Canes as Biomass Crops, INFORMS 2023 Annual Conference. What do you plan to do during the next reporting period to accomplish the goals?1. Finalizing UAS data processing pipeline: We plan to finalize the UAS data processing pipeline for RGB, multi-spectral, and Lidar sensors. (obj 1) Manuscript: Twomanuscripts will be publishedusing this pipeline to obtain canopy height and estimate biomass of energy crops for High-throughput phenotyping. 2. Identify top biomass-producing genotypes (by using all the data collected in year 1 at Weslaco)and establish an experiment at the Corpus Christi location (experiment 2) by inter-cropping cool season leguminous crops. (obj 2) 3. Develop the protocol to measure greenhouse gas measurements and perform environmental impact analysis (obj 3) 4.Develop research-based learning modules for different levels of students:The project team will visit a few more high schools to establish a collaboration for sharing the learning modules with high school teachers. Additionally, in the summer of 2024, the project team will introduce fundamental concepts and knowledge to undergraduate students and K-12 teachers through a Research and Extension Experiences for Undergraduates (REEU) program and a Professional Development for Agricultural Literacy (PDAL) funded by USDA at Texas A&M University-Kingsville. (obj 4) 5. Results will be presented in several national and international conferences.
Impacts
What was accomplished under these goals?
This is the project's first year, and we focused on establishing the experiment during this project period. Following are the details on the progress made: Overall experimental setup: Experimental site:The firstexperiment to evaluate the genotypes for drought tolerance and development of Unmanned Aerial Systems (UAS)-based phenotyping techniques has been established at the Texas A&M AgriLife Research Center at Weslaco (Figure 1). Figure 1. Experimental site Soil sample collection: Before planting, 18 soil samples to 15 cm depth were taken with a foot pedal probe with 2.5 cm diameter and composited to three samples (six in each composite) that were sent to the Texas A&M AgriLife Extension Soil, Water, and Forage Testing Laboratory, College Station, TX. Soils were typical of the calcareous soils of the region with moderate alkalinity (8 pH) with an average of 41 ppm nitrate-N, 32 ppm P, and 443 ppm K. An additional seven samples were taken to 20 cm depth with a 5 cm diameter soil probe to determine bulk density which averaged 1.13 g/cm3. On October 3, 2023, a local NRCS scientist assisted us with deep core sampling at the site. Five samples were collected with a truck probe in each irrigation treatment (dryland, ½, and full irrigation). The probe was 4.1 cm diameter and samples were separated into 10 cm depth increments to facilitate baseline data integration into various crop models and life cycle analyses. The goal was to reach 80 cm, but due to drought, the majority of samples were to 60 cm. One sample was used for bulk density analysis which averaged 1.7 g/cm3 for the top 30 cm. The remaining samples were split to determine soil moisture and sent to Ward Laboratories, Inc. (Kearney, NE) for analyses of pH, electrical conductivity, soil fertility parameters, total N, and organic and total carbon. Figure 2. Soil sampling crew with help from NRCS MLRA Survey Leader Mr. Gary Harris Energy cane genotypes and experimental design: Six energy cane genotypes selected from the Texas A&M AgriLife energy cane germplasm improvement program were planted on August 29, 2023, along with switchgrass and seeded perennial sorghum (n = 5) (Table 1). The field was divided into three blocks for irrigation treatments based on evapotranspiration (ET) needs (100% ET, 50% ET, and rainfed). The experimental design is a completely randomized split-plot design with four replications, with irrigation as a main plot and genotype as a subplot. Plots are 4 rows × 10 m long. Six GoField Plus systems (Goanna Ag. Pvt. Ltd., Queensland Australia) are placed across irrigation treatments. The GoField Plus system consists of a 1 m deep capacitance probe that provides soil moisture and temperature data in 10 cm increments and a single-pixel infrared canopy temperature sensor. Data was reported every hour through a web application along with continuous weather data, growing day degrees, and modeled ET. Based on these measurements, irrigation events as well as the amount to apply are determined. So far, we have not been able to irrigate our crop because of continuous rainfall and low temperatures in the experimental site. Two irrigation events were made right after planting for germination Figure 3. Drip lines for irrigation and GoFieldPlus Systems Table 1: Planted materials along with experimental set-up. Soil moisture sensors: Thirty-three one-meter-deep capacitance probes that provide soil moisture and temperature data in 10 cm increments were installed (one replication in 100% ET, one replication in 50% ET, and two replications in rainfed) to continuously monitor the soil water use. The data are reported using the WoodPecker System webpage and we are currently developing a program to automatically summarize the data atplot level. Figure 4. Soil moisture sensors are placed across the field along with the data visualization webpage. Objective 1: Develop remote sensing-based phenotyping techniques to assess drought tolerance rapidly and non-destructively (in progress) Unmanned Aerial Systems (UAS) data collection and validation: UAS platforms equipped with red-green-blue (RGB), multi-spectral (MS), and LIDAR sensors are being flown weekly to bi-weekly intervals to generate multi-temporal data on crop growth and development. UAS data collection and feature extraction protocols developed by our team for other crops have been successfully tested for energy cane. Additionally, we explored the utility of UAS-LiDAR for phenotyping energy cane crops. Figure 5: Unmanned Aerial Vehicle (UAV) along with different sensors used to collect data Preliminary results from the UAS data: Simple statistical analysis showed a strong correlation between modeled crop height from UAS-LiDAR and field-measured crop height (r= 0.73, p< 0.05, n=48) and biomass (r= 0.83, p< 0.05, n=48), providing empirical relation from which biomass of energy cane could be estimated. While our results provide initial insights, we continue to explore how other features such as vegetation density from UAS-LiDAR can influence the biomass of energy cane. Thermal imagery data will be collected once irrigation differentiation is initiated and plants are under stress. Figure 6:Relationship between LiDAR-derived height, biomass, and field-measured height and biomass Objective 3: Measure the 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. (in progress, the focus will be on years 2 and 3) Create the life cycle inventory database for the two biomass product systems: The team chose OpenLCA as the life cycle analysis (LCA) software since it has open-source databases related to agriculture activities. The LCA framework has been studied and created in OpenLCA. The list of data needed for conducting the LCA studies has been identified. Compared to the previous data collection activities, we will have enough data to conduct the LCA analysis. To test the LCA framework and the functionality of OpenLCA, the project team used data from a previous project to conduct a Well-to-Tank LCA study on energy cane. The results were published as a peer-reviewed abstract and presented at the INFORMS 2023 Annual Conference. Assess the environmental impacts of the two biomass product systems and provide necessary interpretation for stakeholders: The project team has conducted a thorough literature review to identify possible databases, especially life cycle inventory databases, to be used. Objective 4: Train the next-generation bio economy workforce (students and farmers) through an integrated education curriculum (in progress) Develop research-based learning modules for different levels of students: The project team is developing initial learning modules related to drone and life cycle analysis and will test them with K-12 teachers in two summer research programs at Texas A&M University-Kingsville, which will have up to 26 K-12 teachers involved in Summer 2024. Increase farmer's and students' awareness and knowledge of biomass and bioenergy by implementing learning modules in classrooms and webinars: The project team has visited one middle school to showcase the use of drones in agriculture fields.
Publications
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