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001-es BibID:BIBFORM118443
035-os BibID:(Scopus)85183187117
Első szerző:Mirzaei, Morad
Cím:Crop Residues Stimulate Yield-Scaled Greenhouse Gas Emissions In Maize-Wheat Cropping Rotation In A Semi-Arid Climate / Morad Mirzaei, Manouchehr Gorji Anari, Maurício Roberto Cherubin, Nermina Saronjic, Seyed Mohammad Nasir Mousavi, Azin Rooien, Mohammad Zaman, Andrés Caballero-Calvo
Dátum:2024
ISSN:2071-9388 2542-1565
Megjegyzések:Mitigating yield-scaled greenhouse gas emissions (YSE) is beneficial for enhancing crop yield, reducing greenhouse gas (GHG) emissions, and advancing climate-smart agronomic management practices. This study aims to evaluate the impact of different crop residue rates? 100% (R100), 50% (R50), and residue removal (R0) ? on the YSE indicator within a maize-wheat cropping rotation under both conventional tillage (CT) and no-tillage (NT) systems in a semi-arid region. In the NT system, crop residues had a notable effect on the YSE indicator for wheat. Specifically, R0 exhibited a 39% and 20% decrease in YSE for wheat compared to R100 and R50, respectively. Interestingly, crop residue did not significantly influence YSE for maize under the NT system. On the other hand, in the CT system, YSE for maize in R0 was 33% and 25% lower than that in R100 and R50, respectively. Additionally, compared to R0, there were observed increases of 28% and 20% in YSE for wheat in R100 and R50 under the CT system, respectively. Our findings show that crop residue removal decreases YSE under both CT and NT systems. However, given that this practice degrades soil quality and results in lower yields, it is not considered a sustainable management practice compared to residue retention options. This research highlights the importance of evaluating GHG mitigation strategies by concurrently considering both emissions and crop production. Nevertheless, it is essential to conduct off-site assessments of GHG emissions from crop residue application and also engage in long-term studies to comprehend the full potential of crop residue management on YSE.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
climate change
conservation agriculture
cropping system
food security
greenhouse gas emissions
oil health
soil management
Megjelenés:Geography, Environment, Sustainability. - 16 : 4 (2024), p. 125-132. -
További szerzők:Anari, Manouchehr Gorji Cherubin, Maurício Roberto Saronjic, Nermina Mousavi, Seyed Mohammad Nasir (1988-) (agrármérnök) Rooien, Azin Zaman, Mohammad Caballero-Calvo, Andrés
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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
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