Spatial non-stationary characteristics between temperate grasslands based on a mixed geographically weighted regression model

作     者：SONG Xiaolong MI Nan MI Wenbao LI Longtang 宋小龙;米楠;米文宝;李陇堂

作者机构：College of AgricultureNingxia UniversityYinchuan 750021China School of Geography and PlanningNingxia UniversityYinchuan 750021China 

出 版 物：《Journal of Geographical Sciences》 (地理学报（英文版）)

年 卷 期：2022年第32卷第6期

页      面：1076-1102页

核心收录：

学科分类：090503[农学-草业科学] 0909[农学-草学] 0709[理学-地质学] 0905[农学-畜牧学] 09[农学] 0903[农学-农业资源与环境] 0704[理学-天文学] 

基　　金：Ningxia Key R&D Project,No.2018BEB04007 Ningxia Colleges and Universities First-Class Discipline Construction(Grass Science Discipline)Project,No.NXYLXK2017A01 

主　　题：grass yield spatial non-stationary mixed geographically weighted regression model temperate grassland Ningxia 

摘      要：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 *** 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 ***,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)*** results showed that the model was suitable for correlation *** 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 *** influencing factors maintained positive and negative effects on grass yield,save for the strictly negative effect of *** regression results revealed a multiscale differential spatial response regularity of different influencing factors on grass *** parameters revealed that the results of Ordinary least squares(OLS)(Adjusted R^(2)=0.642)and geographically weighted regression(GWR)(Adjusted R^(2)=0.797)models were worse than those of MGWR(Adjusted R^(2)=0.889)*** on the results of the RMSE and radius index,the simulation effect also was MGWRGWROLS ***,the MGWR model held the strongest prediction performance(R^(2)=0.8306).Spatially,the grass yield was high in the south and west,and low in the north and east of the study *** 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-ar