Indian Journal of Agricultural Research

  • Chief EditorV. Geethalakshmi

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Indian Journal of Agricultural Research, volume 44 issue 4 (december 2010) : 316 - 320

UPDATING SOIL CHARACTERISTICS OF ÇUKUROVA REGION (SOUTHERN TURKEY) USING GEOGRAPHICAL INFORMATION SYSTEMS AND ILSEN SOFTWARE

Mahmut Dingil, M. Eren Öztekin, Erhan Akça, Suat Senol
1Çukurova University, Faculty of Agriculture Department of Soil Science and Plant Nutrition, Adana
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Cite article:- Dingil Mahmut, Öztekin Eren M., Akça Erhan, Senol Suat (2024). UPDATING SOIL CHARACTERISTICS OF ÇUKUROVA REGION (SOUTHERN TURKEY) USING GEOGRAPHICAL INFORMATION SYSTEMS AND ILSEN SOFTWARE. Indian Journal of Agricultural Research. 44(4): 316 - 320. doi: .
The soil map of Çukurova University’s (Southern Turkey) campus Adana (1802 ha) was prepared
by conventional air-photo interpretation in 1974, and is updated by employing geographical
information system and remote sensing techniques. Soils of the campus are developed on alluvial
deposits (35%), conglomerates (33%), calcrete (25%) and old river terraces (7%). Soils having 90-
120 cm depth cover 26% of the total area whereas shallow soils (10-30cm) occupy 22%. The large
area (86%) of region overlay on flat (0-2%) or gently sloping lands (2-6%), whereas strongly sloping
lands (12-24%) cover only 14%. Four diagnostic horizons were determined as Bw; Ck; Bw-Ck and
Bt-Ck with 4%, 21%, 12% and 16% distribution. Land Capability Classification (LCC) I, II and III
cover 87.3% of the total geographical area whereas soils of VI and VII class only 12.7%. Soils are
classified into Entisol, Inceptisol, Vertisol and Alfisol orders with 16%, 40%, 26% and 18% distribution.
  1. Ahn, C.W., Baumgardner M.F., and Biehl L.L. (1999) Delineation of soil variability using geostatistics and fuzzy
  2. clustering analyses of hyperspectral data. Soil Sci. Soc. Am. J. 63:142–150.
  3. Bauer J P, Beckett P H T and Bie S W (1972) A rapid gravimetric method for estimating calcium carbonate in soils.
  4. Plant and Soil 37, 689-690.
  5. Bicki, T.J. (1991) Promoting the use of soil survey through the use of improved delivery systems. J. Agron. Educ.
  6. 18:32–36.
  7. Bonta, J.V. (1998) Spatial variability of runoff and soil properties on small watersheds in similar soil-map units. Trans.
  8. ASAE 43:575–585.
  9. Cook, S.E.; Corner, R.J.; Grealish, G.; Gessler, P.E.; Charters, C.J. (1996) A rule-based system to map soil properties.
  10. Soil Science Society of America Journal.; 60: 1893.
  11. Eckenrode, J.J. and Ciolkosz E.J. (1999) Pennsylvania soil survey: The first 100 years. Penn State University Agronomy
  12. Series 144. 24 pp.Gessler, P.E., Moore, I.D., McKenzie, N.J. and Ryan, P.J. (1995) Soil-landscape modelling
  13. and spatial prediction of soil attributes. Int. J. Geographical Information Systems, 9, (4) 421-432.
  14. Gobin, A., P. Campling, J. Deckers, and J. Feyen. (2000) Quantifying soil morphology in tropical environments:
  15. Methods and application in soil classification. Soil Sci. Soc. Am. J. 64:1423–1433.
  16. Lee, B.D., Wald J.A., and Lund L.J.. 1999. Introducing students to online county soil surveys and the STATSGO
  17. database using GIS. J. Nat. Resour. Life Sci. Educ. 28:93–96.
  18. Rogowski, A.S., and J.K. Wolf. 1994. Incorporating variability into soil map unit delineations. Soil Sci. Soc. Am. J.
  19. 58:163–174.
  20. Soil Survey Staff. 2003. Keys to Soil Taxonomy, 9th edition. USDA-Natural Resources Conservation Service, US
  21. Government Printing Office, Washington, DC, USA.
  22. Wagenet, R.J., J. Bouma, and R.B. Grossman. 1991. Minimum data sets for use of soil survey information in soil
  23. interpretive models. p. 161–182. In M.J. Mausbach and L.P. Wilding (eds.) Spatial Variabilities of Soils and
  24. Landforms. SSSA Spec. Publ. No. 28. SSSA, Madison, WI.
  25. Wosten, J.H.M., J. Bouma, and G.H. Stoffelsen. 1985. Use of soil survey data for regional soil water simulation
  26. models. Soil Sci. Soc. Am. J. 49:1238–1244.

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