Study On Soil Organic Carbon Content Of The Second Phase Of Forestry Project In Sandification Combating Program For Area In Vicinty Of Beijing

The Second Phase of Forestry Project in Sandification Combating Program for Area in Vicinty of Beijing is a national key ecologic project.We took soil organic carbon?SOC?in the project area as the research object,based on survey data of 73 plots of the Beijing Sand Source region,analyzed relationship between the content of SOC and 11 site environment factors from the aspects of vegetation,topography and soil.Furthermore,we formulated a regression model of SOC.We also predicted the impact of site environment changes on content of SOC by maximum entropy model?Max Ent?,to explore ways to increase soil organic carbon content in the project area from the perspective of changing site conditions.The results showed that:?1?In the aspect of vegetation factor,the content of SOC has no significant correlation with stand type and stand density,has a negative correlation with shrub biomass and litter.?2?In the term of landform factor,the content of SOC is positively correlated with the altitude,no significant correlation with slope and slope aspect.?3?As for the soil factor,the content of SOC is negatively correlated with p H,and significant positively with available nitrogen?AN?,available potassium?AK?and available phosphorus?AP?.?4?The result of multiple linear regression analysis showed five factors?F₁:soil nutrient;F₂:soil chemical property;F₃:carbon source;F₄:vegetation;F₅:landform?,which extracted from principal component analysis?PCA?,can reflect changes of the content of SOC.The expression of the regression model is:y=13.35F₁+2.17F₂-4.41F₃-1.28F₄-5.20F₅+35.23.?5?The simulation results of the maximum entropy model showed that:the available nitrogen content and available potassium content are the main factors affecting the probability of existence.After increasing the available nitrogen content and available potassium content,the probability of soil organic carbon content in each plot breaking through the survey average is significant improved.