辽宁省农田土壤碳库分布及变化的模拟分析

中国作为世界上一个重要的农业大国，农业土壤对全球大气中CO2浓度的影响正在引起人们的普遗关注。研究以辽宁省为对象，采用针对农业土壤碳库和痕量气体排放估算建立的反硝化分解(DNDC)模型，在建立有关辽宁省气候、农业土壤和农业生产的分县数据库基础上，估算了辽宁省农田土壤碳库储量，为118．55TgC，并发现辽宁省农田土壤碳量有较明显的地区差异，东北地区较高，西南地区较低。经过一年的耕种后，农田土壤碳的变化量也有较明显的地区差异，东北地区减少幅度较大，西南地区相对变化不大或有所增加。还发现无论何种施肥方式，辽宁省农田土壤碳的变化都存在地区差异。且施用有机肥有利于农田土壤碳的积累。在输入的土壤性质数据中，有机质数据的详细程度对DNDC模型模拟结果的精确度影响较大。

A simulation analysis of distribution and change of agricultural soil carbon pool in Liaoning Province

The contribution of China's agricultural practice to atmospheric CO 2 has been paid a great attention because China has a long history of agriculture to support one-fifth of global population. Some authors have thought agricultural soils could sequester a considerable carbon to slow the rise of atmospheric CO 2. In fact, this sequestering potential depend on initial soil content, climate and cultivation. The long-term experiments in North America have proved that arable lands have been changing from a C source to sink by conservation management, such as reduced- and no-till practice, rotation, and introducing cover crops. Herein, by applying a process-oriented model DNDC (Denitrification and decomposition) to assess soil carbon pool and its changes in Liaoning Province. DNDC model has been developed to predict soil carbon and nitrogen biogeochemistry, including soil carbon pool and some trace gas emissions. DNDC was constructed with two components that reflect the driving force which control geochemical and/or soil biochemical processes related to trace gas emissions. The first component, consisting of soil conditions, crop growth and decomposition sub- models, predicts soil temperature, moisture, pH, redox potential (Eh) and substrate concentration profiles based on ecological drivers (e.g., climate, soil, vegetation and anthropogenic activity). The second component, consisting of the nitrification, denitrification and fermentation sub-models, predicts NO, N 2O, CH 4 and NH 3 fluxes based on soil environmental variables. Classical laws of physics, chemistry or biology, or empirical equations generated by previous field studies were used to parameterize each specific reaction in this model. The entire model forms a bridge between basic ecological drivers and trace gas emissions. DNDC model has been tested against a number of field measurements worldwide. Most of the tests showed that DNDC was able to capture general patterns and magnitudes of agricultural soil carbon content measured in fields. In order to apply DNDC to estimate agricultural soil carbon pool and its changes in Liaoning Province, we validated DNDC against the data monitored with two treatments (applying N-fertilizer and manure, respectively) at long-term experiment station of Shenyang Agricultural University. The results showed that DNDC model was suitable for application to estimate the agricultural soil carbon pool in Liaoning Province. The results showed that the agricultural soil carbon content of Liaoning Province was 118.55 (50.8~186.3) TgC under the climatic, soil and farming management conditions in 1990. The regional variation of SOC was significant. SOC content was higher in northeastern parts of Liaoning Province while lower content occurred in southwestern parts. The total changes of agricultural soil carbon content of Liaoning Province were -0.995(-3.17~1.18)TgC per year with regional variation. The amount of the reduction of SOC in northeastern parts of Liaoning Province was relatively large. In the southwestern parts, little reduction or increase of SOC was occurred. We also found that regional variation in change of SOC was occurred under applying N-fertilizer or manure. Applying manure was beneficial to accumulation of agricultural soil carbon. By compared different input data of soil properties, it is showed the degree of details of soil organic matter had great influence on the accuracy of DNDC modeled SOC.