Grazing restoration succession of the degraded grassland is an important aspect in community ecology. Field experiment was carried out to examine how major species restore in first four years restoration following 11 consecutive grazing under different stocking rates in Inner Mongolian steppe. A. frigida and P. acaulis are the most important two species in the all treatments (NG, LG, MG and HG) after four years restoration, although they had high fluctuation. The biomasses of these two species account for 40-90% of total biomass. Especially in no grazing area, which was exclosured since 1990, A. frigida and P. acaulis are still the most important two species in the community. These results suggested A. frigida and P. acaulis conununity are quite stable, and will keep long-time if no special measurements were taken during the restoration of the degraded grassland.

To explore the relationship of species diversity and aboveground productivity in grazing ecosystem is very important to manage grassland. We used the four years' data to check this relationship and to look how abiotic factor affect species diversity and aboveground productivity. We found a good linear relationship between species diversity and aboveground productivity in all previous grazing sites, while no any relationship was found in the no grazing site. From our results, we concluded that drought affects aboveground productivity more than grazing, while heavy grazing affects species diversity more than drought in Inner Mongolian steppe.

The forecasting of container volume which is the basis of port logistics facilities expansion has a great influence on development of an port. Based on this importance, various previous studies have presented methodology on container volume forecasting. The results of many previous studies pointed out the limitations of future forecasting based on past container volume and emphasized that more various factors should be considered to compensate this. Taking notice of this point, this study forecasted future container volume by using ARIMA model, time series analysis and System Dynamics (SD) method, a dynamic analysis technique and performed the comparative review with the forecast of the Ministry of Land, Transport and Maritime affairs. Recently with rapid changes in economic and social environment, the non-linear change tendency for forecasting container traffic is presented as a new alternative to the country.