干旱区生态环境知识资源中心(Arid): Assessment of soil CO2 and NO fluxes in a semi-arid region using machine learning approaches

Arid

DOI	10.1016/j.jaridenv.2023.104947
	Assessment of soil CO2 and NO fluxes in a semi-arid region using machine learning approaches
	Mirzaei, Morad; Caballero-Calvo, Andres; Anari, Manouchehr Gorji; -Pines, Eugenio Diaz; Saronjic, Nermina; Mohammed, Safwan; Szabo, Szilard; Mousavi, Seyed Mohammad Nasir
通讯作者	Mirzaei, M
来源期刊	JOURNAL OF ARID ENVIRONMENTS
ISSN	0140-1963
EISSN	1095-922X
出版年	2023
卷号	211
英文摘要	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.
英文关键词	Agroecosystems Classical regression Climate change Machine learning Iran
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000963237400001
WOS关键词	GREENHOUSE-GAS EMISSIONS ; NITRIC-OXIDE EMISSIONS ; N2O EMISSIONS ; LAND-USE ; TILLAGE ; CARBON ; FOREST ; MODEL ; MINERALIZATION ; IMPLEMENTATION
WOS类目	Ecology ; Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397143
推荐引用方式 GB/T 7714	Mirzaei, Morad,Caballero-Calvo, Andres,Anari, Manouchehr Gorji,et al. Assessment of soil CO2 and NO fluxes in a semi-arid region using machine learning approaches[J],2023,211.
APA	Mirzaei, Morad.,Caballero-Calvo, Andres.,Anari, Manouchehr Gorji.,-Pines, Eugenio Diaz.,Saronjic, Nermina.,...&Mousavi, Seyed Mohammad Nasir.(2023).Assessment of soil CO2 and NO fluxes in a semi-arid region using machine learning approaches.JOURNAL OF ARID ENVIRONMENTS,211.
MLA	Mirzaei, Morad,et al."Assessment of soil CO2 and NO fluxes in a semi-arid region using machine learning approaches".JOURNAL OF ARID ENVIRONMENTS 211(2023).