Use of GIS Geographically Weighted Regression to Determine Natural Rubber Productivity and Their Driving Forces: A Case Study in the Kalutara District of Sri Lanka
Sankalpa, J. K. S. Wijesuriya, W. Karunaratne, S. and Ishani, P. G. N.
ABSTRACT
The goal of this study was to analyze the productivity variation in smallholder rubber lands in Kalutara district located in the wet zone of Sri Lanka and spatial relationship of key drivers to the productivity variation. Low productivity has been a major challenge in rubber plantations in the country in recent years. In this study spatial modelling tools available in geographic information science were used to explore the spatial variability of the rubber productivity and explored the key drivers of it in spatial context. Geostatistical kriging analysis is a simple type of prediction method which includes the cross validation of prediction and error terms in forecasting techniques. The productivity of smallholder rubber lands in Kalutara district varied from 777 to 1463 kg/ha/year, while the highest average productivity was recorded in the Divisional Secretariat (DS) divisions; Palindanuwara, Beruwala and Kalutara. Low productivity was recorded in Matugama and in a few areas in Ingiriya and Bandaragama divisions. Local variation of driving forces behind the average productivity was explored using Geographically Weighted Regression (GWR) method. GWR explored the spatial variability of the relationship between productivity and fertilizer usage, weeding, soil conservation, number of tappable trees and age of trees under tapping. All the variables were found to present significant spatial variabilities. Apart from generating global significant value, the model resulted local variation of each parameter estimates with respect to the projected coordinates of the area. Emerge of sign change of local parameters observed in some areas cannot be observed globally. It is necessary to understand the significance level of local coefficient subject to the multicolinearity and spatial auto correlation.
KEYWORDS: Geographically weighted regression; Kriging geostatistical analyst; Spatial auto correlation