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Field Validation of DNDC Model for Methane and Nitrous Oxide Emissions from Rice-based Production Systems of India
- Babu, Y. Jagadeesh, Li, C., Frolking, S., Nayak, D. R., Adhya, T. K.
- Nutrient cycling in agroecosystems 2006 v.74 no.2 pp. 157-174
- ammonia, carbon dioxide, denitrification, drainage, environmental impact, greenhouse gas emissions, land management, methane, models, nitric oxide, nitrogen, nitrogen dioxide, nitrous oxide, pH, paddies, production technology, seasonal variation, soil texture, testing, India
- The DNDC (DeNitrification and DeComposition) model was tested against experimental data on CH₄ and N₂O emissions from rice fields at different geographical locations in India. There was a good agreement between the simulated and observed values of CH₄ and N₂O emissions. The difference between observed and simulated CH₄ emissions in all sites ranged from −11.6 to 62.5 kg C ha⁻¹ season⁻¹. Most discrepancies between simulated and observed seasonal fluxes were less than 20% of the field estimate of the seasonal flux. The relative deviation between observed and simulated cumulative N₂O emissions ranged from −237.8 to 28.6%. However, some discrepancies existed between observed and simulated seasonal patterns of CH₄ and N₂O emissions. The model simulated zero N₂O emissions from continuously flooded rice fields and poorly simulated CH₄ emissions from Allahabad site. For all other simulated cases, the model satisfactorily simulated the seasonal variations in greenhouse gas emission from paddy fields with different land management. The model also simulated the C and N balances in all the sites, including other gas fluxes, viz. CO₂, NO, NO₂, N₂ and NH₃ emissions. Sensitivity tests for CH₄ indicate that soil texture and pH significantly influenced the CH₄ emission. Changes in organic C content had a moderate influence on CH₄ emission on these sites. Introducing the mid-season drainage reduced CH₄ emissions significantly. Process-based biogeochemical modeling, as with DNDC, can help in identifying strategies for optimizing resource use, increasing productivity, closing yield gaps and reducing adverse environmental impacts.