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

Abstract

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.

Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea

“…Countries such as the Netherlands, the United States and Australia have done lots of research and been in the leading position in the world in this research area (Williams 1990;Jones et al 2003). However, previous studies on crop yield prediction were mainly focusing on food and cash crops by using crop growth models, which need lots of environmental variables because the models simulate the crop growth and development through mathematical functions of environmental conditions and management practices, or grasslands with large area which are suitable for using remote sensing to predict the biomass yield (Hoogenboom et al 2004;Basso et al 2013;Lin and Zhang 2013;Ahn et al 2014). Several studies on using optical devices to predict forage crop biomass were carried out.…”

The Classification Indices-Based Model for NPP According to the Integrated Orderly Classification System of Grassland and Its Application

“…Furthermore, the Classification Indicesbased Model is based on the IOCSG and used the classification indices as independent variables. It not only takes into account dynamic grassland classes [17], but also simulates NPP of corresponding grassland classes [22,24,25,27,28]. Thus it can be very easy to implement and understand.…”

“…It results in a value for NPP (Mg DM ha -1 yr -1 ) as a function of GDD and the moisture index (K value). Its ecological base is the IOCSG [17,20,[22][23][24][25]. The method of integrating the classification indices of IOCSG to estimate the NPP is of the form: from plots to estimate landscape-scale effects, something that so far has proven to be a challenge to ecologists worldwide [37].…”

“…This study continues our efforts to develop a robust estimation capability of NPP for grassland and, specifically, verifies the grassland NPP estimates from the climate-vegetation models along an altitudinal gradient on Helan Mountain and in the surrounding desert rangeland. The specific research objectives are to: 1) compare field-measured NPP based on field-observed data among different grassland classes along an altitudinal gradient in the Helan Mountain range and the surrounding desert; 2) compare and evaluate NPP estimated from the Miami Model [6], Thornthwaite Memorial Model [38], Schuur Model [15], Chikugo Model [8], Beijing Model [12], Synthetic Model [10,11,39], and the Classification Indices-based Model [22,24,25,27,28] with NPP derived from measurements at eight sites in the region; and 3) evaluate the applicability and reliability of the seven models among these grassland classes.…”

“…The total observed NPP dataset was the sum of above-and below-ground NPP, following standard methods [3,40,41], and it was based on averages of five duplicated plots at each sampling site from April to October once every month for three consecutive years (2003)(2004)(2005). One hundred and twenty plot-based NPP sets from the eight study sites were obtained [25].…”