Simulation and prediction of soil organic carbon spatial change in arable lands based on DNDC model

Soil organic carbon (SOC) change not only affects soil fertility and productivity, but also plays an important role to clarify the potential for regional soil carbon sequestration and the impact of global climate change. The method of predicting the SOC trends based on model is superior to methods of long-term experiment and soil sample collecting on the time scale. Because of the spatial and regional variability of climate, soil and farming system, SOC change simulation and prediction on the spatial scale has defect in terms of regional representation and simulation accuracy if using several points or county as modeling unit. In order to improve the accuracy of SOC change prediction on regional and spatial scale, this study simulated the effects of 4 fertilizer application treatments of past 16 years (1990-2005) on SOC change based on DNDC model using data from the long-term experiment station of Gongzhuling of Jilin province in China, and validated the DNDC model. Then the modeling units of Jilin province was divided according to the spatial variability characteristics of climate, soil by using geographic information system (GIS) technology. SOC spatial change trends of Jilin in next 60 years (2011-2070) were simulated and predicted by 3 kinds of scenarios designation on future climate changes (climate repeat, Precis B2 and Precis A2) and spring corn planning. The results showed: (1) DNDC model can be used for SOC simulation and predictions for the study area. The root mean square error (RMSE) were less than 10% comparing SOC measured values to simulated values for 4 fertilizer application treatments. (2) SOC spatial changes simulation and prediction of Jilin based on DNDC model indicated that 60% areas of arable land SOC trended to increase from 2011-2070 under the scenario of climate repeat and spring corn plant, 28% trended to decrease. Under the climate scenario of Precis A2, the area of SOC increase and decrease kept balance. Under the climate scenario of Precis B2, 60% areas of arable land SOC trended to decrease. (3) Under modeling units division by using spatial heterogeneity of climate and soil, the results of SOC simulation and prediction in regional scale are more reasonable as opposed to a few typical points or the county unit as modeling unit.