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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #410035
Research Project: Innovative Cropping System Solutions for Sustainable Production on Spatially Variable Landscapes

Location: Cropping Systems and Water Quality Research

Title: Quantifying carbon sequestration with proximal soil sensors

Author
item Ransom, Curtis
item ROBERTON, STIRLING - Commonwealth Scientific And Industrial Research Organisation (CSIRO)
item FENGKAI, TIAN - University Of Missouri
item JIANFENG, ZHOU - University Of Missouri
item Veum, Kristen
item KITCHEN, NEWELL - Retired ARS Employee
item Sudduth, Kenneth - Ken

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 10/29/2023
Publication Date: 10/29/2023
Citation: Ransom, C.J., Roberton, S., Fengkai, T., Jianfeng, Z., Veum, K.S., Kitchen, N., Sudduth, K.A. 2023. Quantifying carbon sequestration with proximal soil sensors [abstract}. 2023 ASA-CSSA-SSSA International Annual Meeting, October 29-November 1, 2023, St. Louis, Missouri. Available: https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/149923

Interpretive Summary:

Technical Abstract: Agricultural lands can be a sink for carbon and play an important role in offsetting carbon emissions. Current methods of measuring carbon sequestration—through repeated temporal soil samples—are costly and laborious. A promising alternative is using a push probe with sensors [i.e., visible and near-infrared (VNIR) diffuse reflectance spectroscopy, bulk electrical conductivity (ECa), and penetration resistance] to a depth of ~1 meter. However, there is uncertainty around using this technology for estimating carbon in the field, including: 1) how best to relate VNIR data to soil carbon, 2) the number of samples required to estimate carbon stocks under varying management scenarios, and 3) the repeatability of these measurements across time. To answer these questions, data collected between 2014 to 2020 (n > 1,069) from farmer fields were used to optimize deep learning models. Intensive sampling on three adjacent fields (n=700) with contrasting management history was used to determine the total number of samples required to account for spatial variability of carbon. Lastly, repeat samples, after 5+ years, were collected at 100 locations to evaluate the efficacy of quantifying carbon sequestration.