| Carbon dioxide(CO2). the main greenhouse gas in the atmosphere, caused global warming by the rise of its concentration which has a great impact to the global environment, society and economy. Not only is it related to national economic development and energy security, but also it will profoundly influence human living conditions and development in the future. Upland soils accounted for more than 70% of the total area of the farmland soil in China, and soil organic carbon storage is generally low. If reasonable agricultural management was employed, dryland soil has a huge room to grow in our country. However, the accurate estimation of soil organic carbon storage and its variation characteristics is the basis of agricultural carbon sequestration efforts. In this study, taking the DNDC (Denitrification-Decomposition) model which is comparatively mature at modeling biogeochemical process for example, selecting a total of 393x 104 hm2 of upland in the 29 counties (or cities) of North Jiangsu was cited as the study area. Systematic analysis was performed of how the four sources of soil profile data, namely. "Soils of County", "Soils of Prefecture", "Soils of Province" and "Soils of China", and the six scales, i.e.1:50,000,1:250,000,1:500,000,1:1,000,000,1:4,000,000 and 1:10,000,000, used in the 24 soil databases established for the four soil journals, affected simulation of SOC dynamic change by different modeling methods which modeling based on county or polygon as a simulation uint, respectively. Compared with simulation result of the most detailed 1:50,000 soil database established with "Soils of County" which modeling based on polygon as a simulation unit, the other databases established with four sources of soil profile data and six scales whose varied in relative deviation of the simulation of SOCD and SOCS. The results could provide the theoretical supports for appropriate soil data sources, proper mapping scales and reasonable modeling methods in simulating SOC of the country or a region. The main research conclusions were as follows.1. The modeled results of the most detailed 1:50,000 scale soil polygon-based database indicated that an increase of 8.68 Mg C hm"2 in the top layer (0-50 cm) for an area of 393×104 hm2 in the upland soils cultivated with summer maize and winter wheat in the northern Jiangsu Province from 1980 to 2009, with the average annual Soil Organic Carbon Density change of 0.29 Mg C hm-2 y-1. In general, it will be a great potential carbon sink in the uplands of the northern Jiangsu Provinve in the future.2. The SOC dynamic change in the uplands of the northern Jiangsu Provinve was affected by mapping scales and sources of soil data, which modeling based on the simulation unit as a polygon. The soil database of 1:50.000 contained 17024 polygons representing 983 upland soil profiles was selected as a baseline, the relative deviations of the mean SOCD(Soil Organic Carbon Density) and SOCS(Soil Organic Carbon Storage) in other mapping scale databases with various soil profile data sources varied from 0.5%-18.8% and from 0.4%-18.1%, respectively. The relative deviations of the mean SOCD and SOCS from the different mapping scales soil polygon-based database established by "Soils of County" and "Soils of Prefecture" varied from 0.5%-6.3% and from 0.4%-8.6%, respectively. However, the relative deviations of the mean SOCD and SOCS from the different mapping scales soil polygon-based database established by "Soils of Province" and "Soils of China" varied from 6%-19% and from 5%-18%, respectively, which shown a high simulation errors. From the relative deviations of the mean SOCD and SOCS of different sources of soil profile data, with the reduction of mapping scales, the simulation errors were increasing. The results demonstrated that the more detailed soil profile data source and mapping scale databases was necessary to better simulate SOCD and SOCS of upland soils at the regional or national scales.3. The SOC dynamic change in the uplands of the northern Jiangsu Province was severely affected by mapping scales and sources of soil data, which modeling based on the simulation unit as a county. The modeled results of the most detailed 1:50,000 scale soil polygon-based database established by "Soils of County" was selected as a baseline, the relative deviations of the mean SOCD and SOCS in other mapping scale databases with various soil profile data sources varied from 3.7%-64.9% and from 1.0%-66.4%, respectively. In general, with the reduction of soil profile data and mapping scales, the simulation errors were increasing. The results also indicated that the significance of circumstantial soil data sources and precise mapping scales simulate SOCD and SOCS of upland soils of the country or a region.4. There was a significant difference of the simulated results between modeling based on the simulation unit as a county and polygon. Compared with simulation result of the most detailed 1:50,000 soil database established with "Soils of County" which modeling based on polygon as a simulation unit, the simulation errors of the mean SOCD and SOCS in other mapping scale databases with various soil profile data sources which modeling based on the simulation unit as a county, were bigger than the simulation errors of the mean SOCD and SOCS in corresponding mapping scale databases with corresponding soil profile data sources which modeling based on the simulation unit as a polygon. It is also suggested that the simulation of SOC dynamic change did not only need circumstantial soil data sources and precise mapping scales, but also pay full attention to select an appropriate modeling method.5. Initial SOC content was the most sensitive soil factors to the average annual SOCD change of different mapping scales and sources of soil data for the entire study region. Pearson’s test showed that the mean SOCD had significant positive correlation with initial SOC content at the levels of p<0.01, the correlation coefficient varied from 0.919 to 0.990. Further, the multiple linear stepwise regression analysis showed that initial SOC content can explain 84.44%-98.00% of the mean SOCD, while clay content, pH and bulk density had a lesser and unstable effects to the average annual SOCD change of different mapping scales and sources of soil data. |
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