Journal of Geographical Sciences ›› 2022, Vol. 32 ›› Issue (6): 1076-1102.doi: 10.1007/s11442-022-1986-5

• Research article • Previous Articles     Next Articles

Spatial non-stationary characteristics between grass yield and its influencing factors in the Ningxia temperate grasslands based on a mixed geographically weighted regression model

SONG Xiaolong1(), MI Nan1,*(), MI Wenbao1,2, LI Longtang2   

  1. 1.College of Agriculture, Ningxia University, Yinchuan 750021, China
    2.School of Geography and Planning, Ningxia University, Yinchuan 750021, China
  • Received:2021-11-17 Accepted:2022-03-09 Online:2022-06-25 Published:2022-08-25
  • Contact: MI Nan;
  • About author:Song Xiaolong (1991‒), PhD Candidate, specialized in remote sensing monitoring of grassland resources and research on forage-livestock balance. E-mail:
  • Supported by:
    Ningxia Key R&D Project(2018BEB04007);Ningxia Colleges and Universities First-Class Discipline Construction (Grass Science Discipline) Project(NXYLXK2017A01)


Spatial models are effective in obtaining local details on grassland biomass, and their accuracy has important practical significance for the stable management of grasses and livestock. To this end, the present study utilized measured quadrat data of grass yield across different regions in the main growing season of temperate grasslands in Ningxia of China (August 2020), combined with hydrometeorology, elevation, net primary productivity (NPP), and other auxiliary data over the same period. Accordingly, non-stationary characteristics of the spatial scale, and the effects of influencing factors on grass yield were analyzed using a mixed geographically weighted regression (MGWR) model. The results showed that the model was suitable for correlation analysis. The spatial scale of ratio resident-area index (PRI) was the largest, followed by the digital elevation model, NPP, distance from gully, distance from river, average July rainfall, and daily temperature range; whereas the spatial scales of night light, distance from roads, and relative humidity (RH) were the most limited. All influencing factors maintained positive and negative effects on grass yield, save for the strictly negative effect of RH. The regression results revealed a multiscale differential spatial response regularity of different influencing factors on grass yield. Regression parameters revealed that the results of Ordinary least squares (OLS) (Adjusted R2 = 0.642) and geographically weighted regression (GWR) (Adjusted R2 = 0.797) models were worse than those of MGWR (Adjusted R2 = 0.889) models. Based on the results of the RMSE and radius index, the simulation effect also was MGWR > GWR > OLS models. Ultimately, the MGWR model held the strongest prediction performance (R2 = 0.8306). Spatially, the grass yield was high in the south and west, and low in the north and east of the study area. The results of this study provide a new technical support for rapid and accurate estimation of grassland yield to dynamically adjust grazing decision in the semi-arid loess hilly region.

Key words: grass yield, spatial non-stationary, mixed geographically weighted regression model, temperate grassland, Ningxia