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001-es BibID:	BIBFORM107439
035-os BibID:	(cikkazonosító)104947 (WoS)000963237400001 (Scopus)85146558007
Első szerző:	Mirzaei, Morad
Cím:	Assessment of soil CO2 and NO fluxes in a semi-arid region using machine learning approaches / Morad Mirzaei, Manouchehr Gorji Anari , Eugenio Diaz-Pines, Nermina Saronjic, Safwan Mohammed, Szilard Szabo, Seyed Mohammad Nasir Mousavi, Andres Caballero Calvo
Dátum:	2023
ISSN:	0140-1963
Megjegyzések:	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 water-filled 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.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Agroecosystems Classical regression Climate change NOx Machine learning Iran
Megjelenés:	Journal Of Arid Environments. - 211 (2023), p. 1-9. -
További szerzők:	Anari, Manouchehr Gorji Diaz-Pines, Eugenio Saronjic, Nermina Mohammed Safwan (1985-) (agrármérnök) Szabó Szilárd (1974-) (geográfus) Mousavi, Seyed Mohammad Nasir (1988-) (agrármérnök) Caballero-Calvo, Andrés
Pályázati támogatás:	TKP2021-NKTA-32 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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