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  • Title: Development of a method for estimating total CH4 emission from rice paddies in Japan using the DNDC-Rice model.
    Author: Katayanagi N, Fumoto T, Hayano M, Takata Y, Kuwagata T, Shirato Y, Sawano S, Kajiura M, Sudo S, Ishigooka Y, Yagi K.
    Journal: Sci Total Environ; 2016 Mar 15; 547():429-440. PubMed ID: 26802630.
    Abstract:
    Methane (CH4) is a greenhouse gas, and paddy fields are one of its main anthropogenic emission sources. To mitigate this emission based on effective management measures, CH4 emission from paddy fields must be quantified at a national scale. In Japan, country-specific emission factors have been applied since 2003 to estimate national CH4 emission from paddy fields. However, this method cannot account for the effects of weather conditions and temporal variability of nitrogen fertilizer and organic matter application rates; thus, the estimated emission is highly uncertain. To improve the accuracy of national-scale estimates, we calculated country-specific emission factors using the DeNitrification-DeComposition-Rice (DNDC-Rice) model. First, we calculated CH4 emission from 1981 to 2010 using 986 datasets that included soil properties, meteorological data, and field management data. Using the simulated site-specific emission, we calculated annual mean emission for each of Japan's seven administrative regions, two water management regimes (continuous flooding and conventional mid-season drainage), and three soil drainage rates (slow, moderate, and fast). The mean emission was positively correlated with organic carbon input to the field, and we developed linear regressions for the relationships among the regions, water management regimes, and drainage rates. The regression results were within the range of published observation values for site-specific relationships between CH4 emission and organic carbon input rates. This suggests that the regressions provide a simplified method for estimating CH4 emission from Japanese paddy fields, though some modifications can further improve the estimation accuracy.
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