Abstract
Effective verification of soil organic carbon (SOC) improvement interventions through soil carbon sequestration (SCS) requires robust methodologies to measure, report, and verify changes in soil carbon (C) levels. Furthermore, soil C must be monitored over time to ensure that sequestered C is not being re-emitted, thus ensuring the permanence of C removals. The traditional methods for soil C measurement are time-consuming, labor-intensive, and energy-intensive, increasing analysis costs. In this article, we verify the use of a commercially available laser-induced breakdown spectroscopy analyzer, the LaserAg-Quantum, coupled with the recursive feature addition, the gradient-boosted decision trees regression model, and the novelty detection model to predict C in soils. The developed method shows promising performance with an average limit of quantification of 0.75% of C and a precision of 4.10%. Accuracy metrics, including R(2), mean absolute error, and root mean square error, yielded values of 0.81, 0.27%, and 0.37% for the validation dataset. Additionally, around 10% of validation samples after the novelty detection model exhibited relative error greater than 30%. Finally, our findings demonstrate the potential of the LaserAg-Quantum process to support measuring SOC in agricultural soils on a large scale.