Climate change is the biggest concern of the 21st century. Greenhouse gas (GHG) emissions from various sectors are attracting attention as a cause of climate change. The DeNitrification-DeComposition (DNDC) model simulates GHG emissions from cropland. To study future GHG emissions using this simulation model, various factors that could change in future need to be considered. Because most problems are from the agricultural sector, DNDC would be unable to solve the factor-changing problem itself. Hence, it is necessary to link DNDC with separate models that simulate each element. Climate change is predicted to cause a variety of environmental disasters in the future, having a significant impact on the agricultural environment. In the process of human adaptation to environmental change, the distribution and management methods of farmland will also change greatly. In this study, we introduce some drawbacks of DNDC in considering future changes, and present other existing models that can rectify the same. We further propose some combinations with models and development sub-models.

Greenhouse gas emission from agricultural land is recognized as an important factor influencing climatic change. In this study, the national CO2 emission was estimated for paddy soils, using soil GHG emission model (DNDC) with 1 km2 scale. To evaluate the applicability of the model in Korea, verification was carried out based on field measurement data using a closed chamber. The total national CO2 emission in 2015 was estimated at 5,314 kt CO2-eq, with the emission per unit area ranging from 2.2~10.0 t CO2-eq ha-1. Geographically, the emission of Jeju province was particularly high, and the emission from the southern region was generally high. The result of the model verification analysis with the field data collected in this study (n=16) indicates that the relation between the field measurement and the model prediction was statistically similar (RMSE=22.2, ME=0.28, and r2=0.53). More field measurements under various climate conditions, and subsequent model verification with extended data sets, are further required.