A number of studies adapt the DNDC model for use on rice paddy systems.
The DNDC model was adapted to better simulate greenhouse gas emissions from rice paddy ecosystems by Li et al. (2004). Modifications included simulation of anaerobic biogeochemistry and rice growth and parameterisation of paddy rice management. Sensitivity analysis by Li et al. (2004) showed that management practises could significantly affect greenhouse gas emissions, which was affected by soil properties. The most sensitive management practises and soil properties varied with greenhouse gas.
The Most Sensitive Factor (MSF) approach was used to test the model (Li et al., 2004), where the model was run twice for each grid cell with the minimum and maximum values of the most sensitive soil factors observed in each grid cell. The two simulations produced a range, which included the real flux from the grid cell with a high probability. The MSF approach was verified against Monte Carlo analysis for three counties or provinces in China, Thailand or the United States. MSF was found to be a feasible and reliable method 61-99% of the Monte Carlo GHG fluxes were located in the MSF ranges.
Li et al. (2004) ran the adapted DNDC model for all the rice paddies in China under two different water management practises, continuous flooding and mid-season drainage. Under continuous flooding methane (CH4) emissions from the 30 million rice ha of paddy fields was found to range between 6.4 and 12.0 Tg CH4-C per year, whereas under midseason drainage the CH4 flux was reduced to 1.7-7.9 Tg CH4-C. However, shifting water management practise from continuous flooding to mid-season drainage increased nitrous oxide (N2O) emissions by 0.13-0.2 Tg N2O-N/yr and carbon dioxide (CO2) emissions were only slightly altered. The increase in N2O emissions offset approximately 65% of the benefit caused by the decrease in CH4 emissions, as N2O has a radiative forcing more than 10 times greater than CO2.
DNDC was adapted by Fumoto et al. (2008) to explicitly simulate soil processes, crop growth and methane emissions from rice fields under a variety of climatic and agronomic conditions. Rice growth is simulated through tracking photosynthesis, reparation, tillering, C allocation and release of organic C and O2 from roots. The model quantifies the production of electron donors for anaerobic soil processes, by rice root exudation and decomposition. CH4 production and other reductive reactions are simulated based on the availability of electron donors and acceptors. A diffusion routine, based on conductance of tillers and CH4 concentration in soil water, simulates CH4 emission through rice.
The adapted DNDC model produced estimates that were consistent with observations when tested against observed CH4 emissions from 3 rice paddy sites in Japan and China with varying rice residue management and fertilisation (Fumoto et al., 2008). Unlike the original DNDC model, the rice adapted version predicted the negative effect of (NH4)2SO2 on CH4 emission successfully. Although the adapted DNDC gave good predictions of seasonal CH4 emissions, daily CH4 emissions were inaccurate, "suggesting the models immaturity in describing soil heterogeneity or rice-cultivar specific characteristics of CH4 transport". CH4 emissions in a year of low temperatures at one site was overestimated, which indicates uncertainty in root biomass estimates as the model does not consider temperature dependence of leaf area development.
The model can be used to quantitatively estimate CH4 emissions from rice fields under a range of conditions (Fumoto et al., 2008).
Pathak et al. (2005) calibrated and validated the DNDC model against field observations in New Dehli, India. Predicted yield, N uptake and GHG emissions were in agreement with those observed.
A newly compiled soil, climate and landuse database was used to simulate GHG emissions from rice fields in India. Continuous flooding of 42.25ha of rice fields resulted in modelled annual net emissions of 1.07-1.10 Tg of CH4-C, compared to 0.12-0.13 Tg CH4-C with intermittent flooding. CO2-C emissions changed from 21.16-60.96 Tg under continuous flooding to 16.66-48.0 with intermittent flooding. However, N2O-N emissions increased from 0.04-0.05 tp0.05-0.06 Tg N2O-N. Global Warming Potential (GWP) decreased from 130.93-272.83 to 91.73-211.8 Tg CO2 equivalent. Pathak et al. (2008) suggest that the model could be used to estimate GHG emissions and the affect of management, soil and climatic factors on GHG emissions from rice fields in India.
Fumoto et al. (2010) used DNDC-Rice to assess the impact of Alternate Water Regimes on methane emissions from rice fields on a region scale. This used to same model as Fumoto et al. (2008), but the model was thereafter referred to as DNDC-Rice. When tested on three rice fields initally, DNDC-Rice showed acceptable predictions of daily and season methane emissions under different water regimes. A GIS database (rice field area, soil properites, daily weather and farm management) was created for the region scale, which covered 3.2% of rice fields in the Hokkaido region of Japan. To use the model at national scale a database must also be constructed for national scale, as input parameters are highly variable