Evaluation of six methods to predict grassland net primary productivity along an altitudinal gradient in the Alxa Rangeland, Western Inner Mongolia, China

Accurately estimating grassland net primary productivity (NPP) plays an important role in the study of global carbon budgeting. The six different methods (Miami Model, Schuur Model, Chikugo Model, Beijing Model, Synthetic Model and Classification Indices-based Model) were compared in terms of their performance in predicting grassland NPP with NPP derived from field-observed data at eight study sites along an altitudinal gradient in the Helan Mountain range and the surrounding desert in the Alxa Rangeland, Western Inner Mongolia, China. One hundred and twenty plot-based NPP sets from the eight study sites were used, which were obtained from 2003 to 2005, within areas classified as alpine meadow, cold temperate-humid montane meadow, cool temperate-subhumid meadow steppe, cool temperate-semiarid temperate typical steppe and cool temperate-arid temperate zonal semi-desert according to the Integrated Orderly Classification System of Grassland (IOCSG). The relative high model efficiency in predicting grassland NPP using the Classification Indices-based Model and Chikugo Model indicates that these models outperform others. On the basis of input data requirements and the number of free parameters involved in each model, the Classification Indices-based Model was found to be the best choice for the given grassland classes. The results presented in this study were not only specific to this region, but more importantly, were specific to the given grassland classes according to the IOCSG approach, which can be scaled up from plots to estimate landscape-scale effects.