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| Estimation of Grassland Net Primary Production in China with Improved CASA Model Based on the CSCS |
| Zhang Meiling1, Jiang Wenlan2,3, Cheng Quangong4, Liu Xiaoni3 |
1. Center for Quantitative Biology, College of Science, Gansu Agricultural University, Lanzhou 730070, China;
2. Cold and Arid Regions Environments and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China;
3. College of Prataculture, Gansu Agricultural University, Lanzhou 730070, China;
4. College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China |
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Abstract In this study, >0 ℃ average annual cumulative temperature (∑θ) and moisture index (K) in grasslands comprehensive and sequential classification system (CSCS) were introduced into Carnegie Ames Stanford Approach (CASA) model. The net primary production (NPP) of 41 grassland classes in China from 2004 to 2008 was estimated by the improved model. In addition, the temporal and spatial variations of NPP and NPP variations of different grassland classes were analyzed. The results showed that the average values of estimation data of the improved model and the observed data were 503.8 g·-2·a-1and 567.3 g·m-2·a-1, indicating the difference between them was small. The mean error and mean relative error of 41 grassland classes were 4.85 g·m-2·a-1 and 7.6%. There were significant correlations between observed data and estimation data. The estimation data of the improved model was closer to the observed data than that of Miami model and Thornthwaite Memorial model. The grassland NPP in east was higher than that in west, and that in south was higher than that in north. It increased from Northwest China toward Southeast China, which may show the characteristic of horizontal and vertical distribution of ∑θ and K. The trend of grassland NPP in China from 2004 to 2008 was increased and it increased by 23% totally. The variations of annual average NPP in different grassland classes were significant in China from 2004 to 2008, which was consistent with the ∑θ and K for determining grassland class in CSCS. In conclusion, the precision of the improved CASA model is enough for the estimation of grassland NPP. The mutual relation of the classes in CSCS with the simulation of grassland NPP has been completed to a certain extent in this study.
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Received: 12 May 2013
Published: 20 July 2014
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2014, Vol. 34 
