@misc{10481/97235,
year = {2023},
url = {https://hdl.handle.net/10481/97235},
abstract = {Agricultural lands are sources and sinks of greenhouse gases (GHGs). The identification of the main drivers
affecting GHGs is crucial for planning sustainable agronomic practices and mitigating global warming potential.
The main aim of this research was to evaluate the impact of environmental drivers (soil temperature and waterfilled
pore space, WFPS) and crop residue rates on CO2, NO, and NOx fluxes under conventional tillage (CT) and
no-tillage (NT) systems. The accuracy of Random Forest Regression (RFR), Multiple Adaptive Regression Splines
(MARS), and General Linear Models (GLM) in predicting CO2, NO, and NOx fluxes were also assessed.
In both CT and NT systems, CO2, NO, and NOx fluxes decreased with increasing WFPS. Increasing temperature
resulted in higher CO2 emissions and lower NO and NOx emissions. Higher residue rates resulted in significant
increases in CO2 emission, whereas the NO and NOx emissions increased by decreasing the ratio of residue. For
CO2 prediction, the RFR provided the largest R2 with the observed data. For NO–N and NOx-N prediction, RFR
was the most efficient algorithm, but NO–N can be predicted with better accuracy. The output of this research
highlights the importance of agronomic practices for climate mitigation, along with the possibility of using RFR
to predict GHGs fluxes.},
publisher = {Elsevier},
title = {Assessment of soil CO2 and NO fluxes in a semi-arid region using machine learning approaches},
doi = {10.1016/j.jaridenv.2023.104947},
author = {Mirzaei, Morad and Gorji-Anari, M. and Diaz-Pines, E. and Saronjic, N. and Mohammed, S. and Szabo, S. and Nasir-Mousavi, S. and Caballero Calvo, Andrés},
}