Journal of Mountain Science

, Volume 14, Issue 3, pp 492–500 | Cite as

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

Article
  • 80 Downloads

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.

Keywords

Panax ginseng Site suitability Logistic regression Geographically weighted logistic regression Geographic Information System South Korea 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

This study was carried out with the supports of R&D Program for Forestry Technology funded by Korea Forest Service (Project No. S121012L100100) and the framework of international cooperation program funded by National Research Foundation of Korea (2013K2A2A4000649, FY2013).

References

  1. Chen G, Zhao K, McDermid GJ, Hay GJ (2012) The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data. International Journal of Remote Sensing 33: 2909–2924. DOI: 10.1080/01431161.2011.624130CrossRefGoogle Scholar
  2. Choi YE, Kim YS, Yi MJ, et al. (2007) Physiological and chemical characteristics of field-and mountain-cultivated ginseng roots. Journal of Plant Biology 50: 198–205. DOI: 10.1007/BF03030630CrossRefGoogle Scholar
  3. Davis JM (1997) Gineng: a production guide for North Carolina. North Carolina Cooperative Extension Service, North Carolina.Google Scholar
  4. ESRI (2012) ArcGIS Desktop: Release 10. Redland, CA: Environmental Systems Research Institute.Google Scholar
  5. Fleiss JL, Levin B, Cho Paik M (2003) Statistical methods for rates and proportions. New York: John Wiley. Third edition. p 760.CrossRefGoogle Scholar
  6. Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Chichester: Wiley. p 269.Google Scholar
  7. Han H (2014) Development of a management supporting system for mountain ginseng (Panax ginseng) cultivation using spatial information analysis. PhD thesis, Seoul National University, Seoul, Republic of Korea. p 137. (In Korean)Google Scholar
  8. Han H, Jang KM, Song JE, et al. (2011) The effects of site factors on herb species diversity in Kwanneung forest stands. Forest Science and Technology 7: 1–7. DOI: 10.1080/21580103.2011.559942CrossRefGoogle Scholar
  9. Jenness J, Brost B, Beier P (2011) Land facet corridor designer: extention for ArcGIS. Jenness Enterprise, Arizona.Google Scholar
  10. Jeon KS, Youn JH, Park YB, et al. (2013) Standard cultivation guidelines for mountain ginseng. Research Report No. 493. Korea Forest Research Institute. p 49. (In Korean)Google Scholar
  11. Jo DG (2009) GIS and geographically weighted regression in the survey research of small areas. Survey Research 10: 1–19. (In Korean)Google Scholar
  12. Kim C, Choo GC, Cho HS, Lim JT (2015) Soil properties of cultivation sites for mountain-ginseng at local level. Journal of Ginseng Research 39: 76–80. DOI: 10.1016/j.jgr.2014.06.004CrossRefGoogle Scholar
  13. Korea Forest Service (2006) A guide for mountain ginseng cultivation. Daejeon. p 92. (In Korean)Google Scholar
  14. Korea Forest Service (2013) Production of forest products in South Korea. Daejeon. p 588. (In Korean)Google Scholar
  15. Lee DS (2011) The theory and reality for mountain ginseng cultivation. Nexusbooks. p 233. (In Korean)Google Scholar
  16. Lee DS, Woo SY, Choi MS, et al. (2008) Development of optimum cultivation technique and identification technique for the forest ginseng. Deajeon: Korea Forest Service. p 153. (In Korean)Google Scholar
  17. McCune B, Dylan K (2002) Equations for potential annual direct incident radiation and heat load. Journal of Vegetation Science 13: 603–606. DOI:10.1111/j.1654-1103.2002.tb02087.xCrossRefGoogle Scholar
  18. Nakaya T (2016) GWR 4.09 user manual. GWR 4 Development Team. p 39.Google Scholar
  19. R Development Core Team (2014) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
  20. Seo SM (2010) Eco-physiological characteristics and ingredient differences of forest ginseng in various forest environments. PhD thesis, University of Seoul, Seoul, Republic of Korea. p 200. (In Korean)Google Scholar
  21. Seo SM, Woo SY, Lee DS (2007) A study on the photosynthetic rates of Panax ginseng in the different age and provinces. Journal of Korean Forest Society 96: 357–361. (In Korean)Google Scholar
  22. Sørensen R, Seibert J (2007) Effects of DEM resolution on the calculation of topographic indices: TWI and its components. Journal of Hydrology 347: 79–89. DOI: 10.1016/j.jhydrol.2007.09.001CrossRefGoogle Scholar
  23. Wang Q, Ni J, Tenhunen J (2005) Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems. Global Ecology and Biogeography 14: 379–393. DOI: 10.1111/j.1466-822X.2005.00153.xCrossRefGoogle Scholar
  24. Weiss AD (2001) Topographic positions and landform analysis (conference poster). ESRI User Conference, San Diego, CA, USA.Google Scholar
  25. Woo SY, Lee DS (2002) A study on the growth and environments of Panx ginseng in the different forest stands (I). Korean Journal of Agricultural and Forest Meteorology 4: 65–71. (In Korean)Google Scholar
  26. Zhang L, Shi H (2004) Local modelling of tree growth by geographically weighted regression. Forest Science 50: 225–244.Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Forest Engineering, Resources and Management, College of ForestryOregon State UniversityCorvallisUSA
  2. 2.Department of Forest Sciences, College of Agriculture and Life SciencesSeoul National UniversitySeoulRepublic of Korea
  3. 3.Research Institute for Agriculture and Life SciencesSeoul National UniversitySeoulRepublic of Korea

Personalised recommendations