A mixed application of Geographically Weighted Regression and unsupervised classification for analyzing latex yield variability in yunnan, China
This paper introduces a mixed method approach for analyzing the determinants of natural latex yields and the associated spatial variations and identifying the most suitable regions for producing latex. GeographicallyWeighted Regressions (GWR) and Iterative Self-Organizing Data Analysis Technique (ISODATA) are jointly applied to the georeferenced data points collected from the rubber plantations in Xishuangbanna (in Yunnan province south China) and other remotely-sensed spatial data. According to the GWR models Age of rubber tree Percent of clay in soil Elevation Solar radiation Population Distance from road Distance from stream Precipitation and Mean temperature turn out statistically significant indicating that these are the major determinants shaping latex yields at the prefecture level. However the signs and magnitudes of the parameter estimates at the aggregate level are different from those at the lower spatial level and the differences are due to diverse reasons. The ISODATA classifies the landscape into three categories: high medium and low potential yields. The map reveals that Mengla County has the majority of land with high potential yield while Jinghong City and Menghai County show lower potential yield. In short the mixed method can offer a means of providing greater insights in the prediction of agricultural production. © 2017 by the authors.
Authors
Kim, O.S..,Nugent, J.B.,Yi, Z.-F.,Newell, J.P.,Curtis, A.J.
Publication year
2017
