Estimation of pasture productivity in Mongolian grasslands: field survey and model simulation

Abstract

The Mongolian economy depends critically on products of range-fed livestock. Pasture is the major food source for livestock grazing, and its productivity is strongly affected by climatic variability. Direct measurement of pasture productivity is time-consuming and difficult, especially in remote areas of a large country like Mongolia with sparse spatial distribution of pasture monitoring. Therefore, modeling is a valuable tool to simulate pasture productivity. In this study, we used a remote sensing-based production efficiency model, that is, the Carnegie Ames Stanford Approach (CASA) model, to estimate pasture productivity in three main vegetation zones of Mongolia; desert steppe, steppe and forest steppe. The present study aimed to explore climatic and grazing effects on grassland productivity in Mongolian grasslands during 2005-2007, using ground-based measurements and simulation model outputs.
The ground measurements showed that grazing caused a significant decrease in measured aboveground phytomass and plant height. Simulation results demonstrated that the highest net primary productivity (NPP) of 83.2 gC/m² and the lowest NPP of 12.6 gC/m² over the growing season (April-September) occurred at Darkhan (steppe) and Mandalgovi (desert steppe), respectively. Moreover, the comparison of temperature and water stresses on pasture productivity indicated that water stress was stronger downregulator of NPP, verifying that drought is the major concern of pasture production. Based on the comparison between the measurements and simulation, the ratio of aboveground NPP to belowground NPP in the Mongolian perennial grasslands was estimated as 1:1.5.