Spatio-Temporal Variation and the Associated Factor Analysis of Net Primary Productivity in Grasslands in Inner Mongolia

Abstract

The grassland ecosystem in the Inner Mongolia Autonomous Region (IMAR) serves as a vital ecological barrier in northern China, and the vegetation productivity in the grasslands exhibits considerable temporal and spatial variations. However, few studies have examined the long-term variations in the NPP in the IMAR and quantified the effects of natural factors and human activities on the NPP. The study modeled the net primary productivity (NPP) of the IMAR’s grasslands using the Carnegie–Ames–Stanford approach (CASA) model and employed linear regression, trend analysis, and spatial statistics to analyze the spatio-temporal patterns in vegetation productivity and explore the impact on the NPP of natural and socio-economic factors over the past two decades. The results reveal that the average NPP value from 2001 to 2021 was 293.80 gC∙m−2 a−1, characterized by spatial clustering of a relatively high NPP in the east, a low NPP in the west, and an annual increase of 3.26 gC∙m−2 over the years. The NPP values varied significantly across different vegetation cover types, with meadows having the highest NPP, followed by typical steppe and desert grasslands. The spatial distribution pattern and temporal changes in the grassland productivity are the result of both natural factors and human activities, including topographical properties and socio-economic indicators such as gross domestic product, night-time light, and population. The results for the NPP in the IMAR were based solely on the CASA model and, therefore, to achieve improved data reliability, exact measurements in real field conditions will be conducted in the future. The findings from the spatial clustering and temporal trajectories of the NPP and the impacts from the factors can provide useful guidance to planning grassland vegetation protection policies for the IMAR.