Anthropogenic activities profoundly influence carbon sequestration in the Eurasian Steppe.  In particular, grazing has been identified as having a major effect on carbon sequestration.  However, the extent to which grazing affects regional patterns or carbon sequestration is unknown.  In this study, we evaluated the impact of regional grazing on grassland carbon sequestration using the Boreal Ecosystem Productivity Simulator (BEPS) and the Shiyomi grazing model.  Model performances were validated against the results from field measurements and eddy covariance (EC) sites.  Model outputs showed that in 2008, the regional net primary productivity (NPP) was 79.5 g C m^–2, and the net ecosystem productivity (NEP) was –6.5 g C m^–2, characterizing the region as a weak carbon source.  The Mongol Steppe (MS) was identified as a carbon sink, whereas the Kazakh Steppe (KS) was either carbon neutral or a weak carbon source.  The spatial patterns of grazing density are divergent between the MS and the KS.  In the MS, livestock was mainly distributed in China with relatively good management, while in the KS livestock was mainly concentrated in the southern countries (especially Uzbekistan and Turkmenistan) with harsh environments and poor management.  The consumption percentages of NPP in Turkmenistan, Tajikistan and Uzbekistan were 5.3, 3.3 and 1.2%, respectively, whereas the percentages in other countries were lower than 1%.  Correspondingly, grazing consumption contributed to the carbon sources of Turkmenistan, Tajikistan and Uzbekistan by 11.6, 6.3 and 4.3%, respectively, while it weakened the carbon sink in Inner Mongolia, China and Mongolia by 1.6 and 0.5%.  This regional pattern should be affected by different sub-regional characteristics, e.g., the continuous degradation of grassland in the southern part of the KS and the restoration of grassland in Inner Mongolia, China.

In the study, an improved approach was proposed to identify the contribution shares of three group factors that are climate, technology and input, social economic factors by which the grain production is shaped. In order to calibrate the method, Jiangxi Province, one of the main paddy rice producers in China was taken as an example. Based on 50 years (1961-2010) meteorological and statistic data, using GIS and statistical analysis tools, the three group factors that in certain extent impact China’s paddy rice production have been analyzed quantitatively. The individual and interactive contribution shares of each factor group have been identified via eta square (η2). In the paper, two group ordinary leasr square (OLS) models, paddy models and climate models, have been constructed for further analysis. Each model group consists of seven models, one full model and six partial models. The results of paddy models show that climate factors individually and interactively contribute 11.42-15.25% explanatory power to the variation of paddy rice production in the studied province. Technology and input factors contribute 16.17% individually and another 8.46% interactively together with climate factors, totally contributing about 25%. Social economic factors contribute about 7% of which 4.65% is individual contribution and 2.49% is interactive contribution together with climate factors. The three factor groups individually contribute about 23% and interactively contribute additional 41% to paddy rice production. In addition every two of the three factor groups also function interactively and contribute about 22%. Among the three factor groups, technology and input are the most important factors to paddy rice production. The results of climate models support the results of paddy models, and display that solar radiation (indicated by sunshine hour variable) is the dominate climate factor for paddy rice production.

Changes in drought trends and its relationship with climate change in China were examined in this study. The temperature vegetation drought index (TVDI) datasets recorded during 2001 to 2010 in China were constructed by using the moderateresolution imaging spectroradiometer (MODIS) data, and the temporal and spatial variations in drought were analyzed. In addition, the mean temperature, mean precipitation, mean relative humidity, and mean sunshine duration data collected from 557 local weather stations in China were analyzed. The relationships between drought and these climate factors were also analyzed by using correlation analysis and partial correlation analysis. Changes in drought tendency were shown to differ among four regions throughout the country. The fluctuations in climate in the regions of northeastern China, Qinling-Huaihe, and central Qinghai were caused by the increase in soil moisture, and that in southern Tibet was caused by the intensification of drought. Meteorological factors exhibited varied effects on drought among the regions. In southern China, the main influential factor was temperature; other factors only showed minimal effects. That in the northern and northwestern regions was sunshine duration, and those in the Qinghai-Tibet Plateau were relative humidity and temperature.