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Comparison between Spatial Interpolation Methods of Temperature Data for Garlic Cultivation

마늘 재배적지분석을 위한 기온자료 공간보간기법 비교

  • 김용완 (경상대학교 대학원) ;
  • 홍석영 (국립농업과학원 농업환경부) ;
  • 장민원 (경상대학교 농업생명과학대학 지역환경기반공학과, 경상대학교 농업생명과학연구원)
  • Received : 2011.05.27
  • Accepted : 2011.08.08
  • Published : 2011.09.30

Abstract

The objective of this study is to decide a spatial interpolation method on temperature data for the suitability analysis of garlic cultivation. In Korea, garlic is the second most cultivated condiment vegetable after red pepper. Nowadays warm-temperate garlic faces potential shift of its arable area according to warmer temperature in the Korean Peninsula, and the change can be drawn with the precise temperature map derived from interpolation on point-measured data. To find the preferable interpolation method in cases of germination and vegetative period of the garlic, different approaches were tested as follows: Inverse Distance Weighted (IDW), Spline, Ordinary Kriging (OK), and Universal Kriging (UK). As a result, IDW and UK show the lowest root mean square errors as for the germination and vegetative seasons, respectively. However, statistically significant difference was not revealed among the applied methods regarding the germinating period. Eventually this will contribute to mapping the suitable lands for the cultivation of warm-temperate garlic reasonably.

Keywords

References

  1. Anderson, S., 2001. An Evaluation of Spatial Interpolation Methods on Air Temperature in Phoenix, AZ. http://www.cobblestoneconcepts.com. Accessed 25 May. 2011.
  2. Cho, H. L. and J. C. Jeong, 2006. Application of Spatial Interpolation to Rainfall Data. The Journal of GIS Association of Korea, 14(1): 29-41. (in Korean)
  3. ESRI, 2001. ArcGIS Geostatistical Analyst: Statistical Tools for Data Exploration, Modeling and Advanced Surface Generation. http://www.esri.com. Accessed 25 May. 2011.
  4. ESRI, 2003. ArcGIS 9 Using ArcGIS Geostatistical Analyst. Independent Pub Group.
  5. Food and Agriculture Orgaization (FAO) 2009. Http://faostat.fao.org. Accessed 26 May. 2011.
  6. Hubert, H., K. Merganicova, R. Petritsch, S. A. Pietsch and P. E. Thornton, 2003, Validating daily climate interpolations over complex terrain in Austria. Agricultural and Forest Meteorology 119: 87-107 https://doi.org/10.1016/S0168-1923(03)00114-X
  7. Johnston, K., J. M. Ver Hoef, K. Krivoruchko and N. Lucas, 2001. Using ArcGIS Spatial Analyst, ESRI Press.
  8. Korea Meteorological Administration, 2010. Climatic change of Daegu. Http://web.kma.go.kr. Accessed 25 May. 2011. (in Korean)
  9. Korean statistical information service, 2010. Production of vegetable. Http://www.kosis.kr. Accessed 25 May. 2011. (in Korean)
  10. Lee, H. S., 2010. Comparison and Evaluation of Root Mean Square for Parameter Settings of Spatial Interpolation Method. Jurnal of the Korean Association of Geographic Information Studies 13(3): 29-41 (in Korean)
  11. Lin Z. H., X. G. MO, H. X. LI and H. B. LI, 2002. Comparison of Three Spatial Interpolation Methods for Climate Variables in China. Acta Geographical Sinica. 57(1): 47-56
  12. Namhae Garlic Research Institute, 2010. Effect of garlic. http://www.namhaegarlic.or.kr. Accessed 25 May. 2011. (in Korean)
  13. Piazza A. D., F. L. Conti, L. V. Noto, F. Viola, and G. L. Loggia, 2011. Comparative Analysis of Different Techniques for Spatial Interpolation of Rainfall Data to Create a Serially Complete Monthly Time Series of Precipitation for Sicily, Italy. International Journal of Applied Earth Observation and Geoinformation 13: 396-408 https://doi.org/10.1016/j.jag.2011.01.005
  14. Rural Development Administration, 2010. Agricultural Technology. Http://www.rda.go.kr. Accessed 27 May. 2011.
  15. Snell, S. E., S. Gopal and R. K. Kaufmann, 2000. Spatial Interpolation of Surface Air Temperature Using Artificial Neural Networks: Evaluating Their Use for Downscaling GCMs. Journal of Climate 13(5): 886-895. https://doi.org/10.1175/1520-0442(2000)013<0886:SIOSAT>2.0.CO;2

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