Identification of greenhouse gas hot-spots and predicting district-wise GHG Intensities in rice based crop rotations in India using DNDC model

Abstract:

Indian scientific research on rice cultivation has been targeted primarily at enhancing crop productivity for the wide range of soil and climate conditions across India. With the understanding that rice paddies are major source of atmospheric CH₄ and N₂O, there is a need for careful evaluation of the source strength of this ecosystem, and the influence of soil, water and crop management practices on both CH₄ and N₂O fluxes. A major challenge in meeting this objective lies in reducing the large uncertainties associated with regional and global level estimates of GHG emissions. Process-based biogeochemical models like DNDC (Denitrification and Decomposition model) can provide important insights into how agricultural management of rice paddies influences water resources, yields and greenhouse gas emissions. To quantify the CH₄ and N₂­O emissions from rice based crop rotations of India, DNDC model was modified, calibrated and evaluated at different sites across India. The observed N uptake, yields, CH₄ and N₂O emissions were in agreement with the values predicted by the model. To drive the model across India, a unique database was prepared combining soil/climate/landuse and management information. By linking the spatial database to DNDC, CH₄ and N₂O emissions from Indian rice based crop rotations for the rice growing season in the year 2000 were simulated on day-by-day basis. CH₄ and N₂O emissions from rice paddies in India from rice based-growing season were estimated to be 3.8 and 0.1 - 0.08 Tg (1 Tg = 10¹² gm). Of the total CH₄ emissions 30.6% (1.2 Tg) is emitted from single irrigated rice. 30.0% (1.1 Tg) and 14.46%(0.56 Tg) of total CH₄ is emitted from double rice and single rainfed rice cropping system. 32.9% (33.16 Gg) of the total N₂O emissions is emitted by upland rice followed by Single rainfed rice cropping system which emits about 19.9%(20.06 Gg) of total N₂O . This paper presents a framework on GIS databases and a process-based biogeochemical model for estimation of greenhouse gas emissions from Indian based crop rotations.