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ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Crop Germplasm Research » Research » Publications at this Location » Publication #367677

Research Project: Genetic Improvement of Perennial Warm-Season Grasses as Forage, Bioenergy, Turf, and Value-added Bioproducts within Sustainable Cropping Systems

Location: Crop Germplasm Research

Title: Quantification of soil organic carbon in biochar amended soil using ground penetrating radar (GPR)

item SHEN, XIAOQING - Texas A&M University
item FOSTER, TYLER - Texas A&M University
item BALDI, HEATHER - Texas A&M University
item DOBREVA, ILLIYANA - Texas A&M University
item Burson, Byron
item HAYS, DIRK - Texas A&M University
item TABIEN, RODANTE - Texas A&M Agrilife
item JESSUP, RUSSELL - Texas A&M University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/25/2019
Publication Date: 12/3/2019
Citation: Shen, X., Foster, T., Baldi, H., Dobreva, I., Burson, B.L., Hays, D., Tabien, R., Jessup, R. 2019. Quantification of soil organic carbon in biochar amended soil using ground penetrating radar (GPR). Remote Sensing. 11(23):2874.

Interpretive Summary: Biochar is a charcoal-like material that is produced when plant vegetative materials are exposed to elevated temperatures in an oxygen limited controlled environment. This process is known as pyrolysis, and the resulting biochar is a stable form of carbon from which very little escapes into the atmosphere. Consequently, the conversion of plant biomass into biochar can reduce the release of greenhouse gases into the atmosphere and serves as a means of carbon sequestration which helps mitigate climate change. Biochar also has been known to improve the quality and productivity of soils by enhancing soil structure, increasing water retention and aggregation, decreasing acidity, improving electrical conductivity, and increasing soil microbe abundance. Because of these positive attributes of biochar, we conducted an experiment to determine if the organic carbon in biochar added to a sandy soil can be measured and monitored using ground penetrating radar (GPR). The carbon sources could not be visually detected because of their small particle sizes, but GPR attributes analyses and a predictive mathematical model were able to provide information regarding the carbon and moisture content of the different treatments and carbon sources evaluated. These findings indicate that GPR can be used to detect differences in both carbon content and structure in a sandy soil. Future research using GPR for monitoring organic carbon in soils should be expanded to include a range of diverse soil types.

Technical Abstract: The application of biochar amendments to soil has been proposed as a strategy for mitigating global carbon (C) emissions and soil organic carbon (SOC) loss. Biochar can provide additional agronomic benefits to cropping systems, including improved crop yield, soil water holding capacity, seed germination, cation exchange capacity (CEC), and soil pH. Commercial development of biochar amendments has been limited; however, their positive potential impact emphasizes the need for additional research. To maximize the beneficial effects of biochar amendments towards the inventory, increase, and management of SOC pools, non-destructive analytical methods such as ground penetrating radar (GPR) are needed to identify and quantify below ground C. The use of GPR has been well characterized across geological, archaeological, engineering, and military applications. While GPR has been predominantly utilized to detect relatively large objects such as rocks, tree roots, land mines, and peat soils, the objective of this study was to quantify comparatively smaller, particulate sources of SOC. This research used three materials as C sources: biochar, graphite, and activated C. The C sources were mixed with sand - 12 treatments in total and scanned under three moisture levels: 0%, 10%, and 20% to simulate different soil conditions. GPR attribute analyses and naïve Bayes predictive models were utilized in lieu of visualization methods because of the minute size of the C particles. Significant correlations between GPR attributes and both C content and moisture levels were detected. The prediction accuracy of a predictive model for C content ranged from 55% to 70%, and the accuracy for C structure ranged from 50% to 65%. The prediction accuracy confirmed the ability of GPR to identify differences in both C content and C structure. Beneficial future applications could focus on applying GPR across more diverse soil conditions.