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Selecting suitable sites for mountain ginseng (Panax ginseng) cultivation by using geographically weighted logistic regression

Selecting suitable sites for mountain ginseng (Panax ginseng) cultivation by using geographically... With the well-being trends to pursue a healthy life, mountain ginseng (Panax ginseng) is rising as one of the most profitable forest products in South Korea. This study was aimed at evaluating a new methodology for identifying suitable sites for mountain ginseng cultivation in the country. Forest vegetation data were collected from 46 sites and the spatial distribution of all sites was analyzed using GIS data for topographic position, landform, solar radiation, and topographic wetness. The physical and chemical properties of the soil samples, including moisture content, pH, organic matter, total nitrogen, exchangeable cations, available phosphorous, and soil texture, were analyzed. The cultivation suitability at each site was assessed based on the environmental conditions using logistic regression (LR) and geographically weighted logistic regression (GWLR) and the results of both methods were compared. The results show that the areas with northern aspect and higher levels of solar radiation, moisture content, total nitrogen, and sand ratio are more likely to be identified as suitable sites for ginseng cultivation. In contrast to the LR, the spatial modeling with the GWLR results in an increase in the model fitness and indicates that a significant portion of spatial autocorrelation in the data decreases. A higher value of the area under the receiver operating characteristic (ROC) curve presents a better prediction accuracy of site suitability by the GWLR. The geographically weighted coefficient estimates of the model are non-stationary, and reveal that different site suitability is associated with the geographical location of the forest stands. The GWLR increases the accuracy of selecting suitable sites by considering the geographical variations in the characteristics of the cultivation sites. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Mountain Science Springer Journals

Selecting suitable sites for mountain ginseng (Panax ginseng) cultivation by using geographically weighted logistic regression

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References (38)

Publisher
Springer Journals
Copyright
Copyright © 2017 by Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg
Subject
Earth Sciences; Earth Sciences, general; Geography, general; Environment, general; Ecology
ISSN
1672-6316
eISSN
1993-0321
DOI
10.1007/s11629-016-4118-9
Publisher site
See Article on Publisher Site

Abstract

With the well-being trends to pursue a healthy life, mountain ginseng (Panax ginseng) is rising as one of the most profitable forest products in South Korea. This study was aimed at evaluating a new methodology for identifying suitable sites for mountain ginseng cultivation in the country. Forest vegetation data were collected from 46 sites and the spatial distribution of all sites was analyzed using GIS data for topographic position, landform, solar radiation, and topographic wetness. The physical and chemical properties of the soil samples, including moisture content, pH, organic matter, total nitrogen, exchangeable cations, available phosphorous, and soil texture, were analyzed. The cultivation suitability at each site was assessed based on the environmental conditions using logistic regression (LR) and geographically weighted logistic regression (GWLR) and the results of both methods were compared. The results show that the areas with northern aspect and higher levels of solar radiation, moisture content, total nitrogen, and sand ratio are more likely to be identified as suitable sites for ginseng cultivation. In contrast to the LR, the spatial modeling with the GWLR results in an increase in the model fitness and indicates that a significant portion of spatial autocorrelation in the data decreases. A higher value of the area under the receiver operating characteristic (ROC) curve presents a better prediction accuracy of site suitability by the GWLR. The geographically weighted coefficient estimates of the model are non-stationary, and reveal that different site suitability is associated with the geographical location of the forest stands. The GWLR increases the accuracy of selecting suitable sites by considering the geographical variations in the characteristics of the cultivation sites.

Journal

Journal of Mountain Science – Springer Journals

Published: Mar 2, 2017

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