# Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands
## 5 Conclusions  
This work quantified the temporal and spatial effects and the relative impacts of recent climate change and grazing activities on the NEE for each country or region (Xinjiang), by using the Biome-BGC grazing model forced by three climate datasets (CFSR, ERA-Interim, and MERRA). During 1981-2014, Central Asia experienced a significant warming trend (0.03 \(\pm\) 0.01 \({}^{\circ}\)C year\({}^{-1}\)) and a drying trend (\(-\)2.18 \(\pm\) 2.04 mm year\({}^{-1}\)), and the average annual precipitation in the CFSR was 45.14% and 80.47%, higher than that in the ERA-Interim and the MERRA, respectively. The simulated annual total NEE is \(-\)0.06 \(\pm\) 0.04 Pg C year\({}^{-1}\), which equals to a total carbon source of \(-\)2.10 \(\pm\) 1.60 Pg C in the pasturelands of Central Asia in 1981-2014. The NEE varied among the three simulations; the highest NEE was obtained with the CFSR (\(-\)15.42 g C m\({}^{-2}\) year\({}^{-1}\)) and the lowest with the MERRA (\(-\)23.69 g C m\({}^{-2}\) year\({}^{-1}\)). In addition, we found that climate changes dominated carbon assimilation, while grazing resulted in 100% of carbon release, and its relative contribution differed among grassland types, which indicates that effective and regional-specific grazing controls should be implemented. For the long run, this study could serve as a possible reference for decision makers, social organizations, and individuals, in light of regional sustainable development and the international CO2 trading. A more systemic, interdisciplinary approach to evaluate the carbon budget of Central Asia is promising for future studies. Conceptualization, methodology, formal analysis, visualization, and writing--original draft preparation, C.L. and W.X. ; methodology, software, and writing--review and editing, Q.H. ; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript. This research is supported by the Open Foundation of State Key Laboratory of Desert and Oasis Ecology, the Xinjiang Institute of Ecology and Geography, the Chinese Academy of Sciences (G2019-02-03), the Natural Science Foundation of Jiangsu Province, China (BK20201393), and the National Natural Science Foundation of China (NO. 42177436). The CFSR, ERA-Interim, and MERRA reanalysis data are available at [[https://www.hycom.org/datasetver/ncep-cfsr](https://www.hycom.org/datasetver/ncep-cfsr)]([https://www.hycom.org/datasetver/ncep-cfsr](https://www.hycom.org/datasetver/ncep-cfsr)) (accessed 20 March 2017), [[https://disc.gsfc.nasa.gov/datasets](https://disc.gsfc.nasa.gov/datasets)]([https://disc.gsfc.nasa.gov/datasets](https://disc.gsfc.nasa.gov/datasets)) (accessed 23 March 2017) and [[https://disc.gsfc.nasa.gov/datasets](https://disc.gsfc.nasa.gov/datasets)]([https://disc.gsfc.nasa.gov/datasets](https://disc.gsfc.nasa.gov/datasets)) (accessed 28 March 2017), respectively. Soil texture data are available at [[http://webarchive.iiaasa.ac.at/Research/LUC/External-World-soil-database/HTML/HWSD_Data.html?sb=4](http://webarchive.iiaasa.ac.at/Research/LUC/External-World-soil-database/HTML/HWSD_Data.html?sb=4)]([http://webarchive.iiaasa.ac.at/Research/LUC/External-World-soil-database/HTML/HWSD_Data.html?sb=4](http://webarchive.iiaasa.ac.at/Research/LUC/External-World-soil-database/HTML/HWSD_Data.html?sb=4)) (accessed 10 May 2017). Atmospheric CO2 concentrations were derived from observations taken at Mauna Loa (f[[https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_anmmean_mlo.txt](https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_anmmean_mlo.txt)]([https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_anmmean_mlo.txt](https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_anmmean_mlo.txt)); accessed 25 April 2017).