Korean Journal of Soil Science and Fertilizer (한국토양비료학회지)

Korean Society of Soil Science and Fertilizer (한국토양비료학회)
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Use of the Quantitatively Transformed Field Soil Structure Description of the US National Pedon Characterization Database to Improve Soil Pedotransfer Function

  • Yoon, Sung-Won (National Academy of Agricultural Science, Rural Development Administration) ;
  • Gimenez, Daniel (Department of Environmental Sciences, Rutgers,The State University of New Jersey) ;
  • Nemes, Attila (Univ. of Maryland, Dep. of Plant Science and Landscape Architecture 2102 Plant Science Building College Park) ;
  • Chun, Hyen-Chung (National Academy of Agricultural Science, Rural Development Administration) ;
  • Zhang, Yong-Seon (National Academy of Agricultural Science, Rural Development Administration) ;
  • Sonn, Yeon-Kyu (National Academy of Agricultural Science, Rural Development Administration) ;
  • Kang, Seong-Soo (National Academy of Agricultural Science, Rural Development Administration) ;
  • Kim, Myung-Sook (National Academy of Agricultural Science, Rural Development Administration) ;
  • Kim, Yoo-Hak (National Academy of Agricultural Science, Rural Development Administration) ;
  • Ha, Sang-Keun (National Academy of Agricultural Science, Rural Development Administration)
  • Received : 2011.07.20
  • Accepted : 2011.10.13
  • Published : 2011.10.31
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Abstract

Soil hydraulic properties such as hydraulic conductivity or water retention which are costly to measure can be indirectly generated by soil pedotransfer function (PTF) using easily obtainable soil data. The field soil structure description which is routinely recorded could also be used in PTF as an input to reduce the uncertainty. The purposes of this study were to use qualitative morphological soil structure descriptions and soil structural index into PTF and to evaluate their contribution in the prediction of soil hydraulic properties. We transformed categorical morphological descriptions of soil structure into quantitative values using categorical principal component analysis (CATPCA). This approach was tested with a large data set from the US National Pedon Characterization database with the aid of a categorical regression tree analysis. Six different PTFs were used to predict the saturated hydraulic conductivity and those results were averaged to quantify the uncertainty. Quantified morphological description was successively used in multiple linear regression approach to predict the averaged ensemble saturated conductivity. The selected stepwise regression model with only the transformed morphological variables and structural index as predictors predicted the $K_{sat}$ with $r^2$ = 0.48 (p = 0.018), indicating the feasibility of CATPCA approach. In a regression tree analysis, soil structure index and soil texture turned out to be important factors in the prediction of the hydraulic properties. Among structural descriptions size class turned out to be an important grouping parameter in the regression tree. Bulk density, clay content, W33 and structural index explained clusters selected by a two step clustering technique, implying the morphologically described soil structural features are closely related to soil physical as well as hydraulic properties. Although this study provided relatively new method which related soil structure description to soil structure index, the same approach should be tested using a datasets containing the actual measurement of hydraulic properties. More insight on the predictive power of soil structure index to estimate hydraulic properties would be achieved by considering measured the saturated hydraulic conductivity and the soil water retention.

Keywords

References

  1. Bouma, J., and J.L. Anderson. 1997. Water movement through pedal soils. I. Saturated flow. Soil Sci. Soc. Am. J. 41:413-418.
  2. Brakensiek, D.L., W.J. Rawls, and G.R. Stephenson. 1984. Modifying SCS hydrologic soil groups and curve numbers for rangeland soils. ASAE Paper No. PNR-84-203, St. Joseph,MI.
  3. Calero, J., R. Delgado, G. Delgado, and J.M. Martin-Garcia. 2008. Transformation of categorical field soil morphological properties into numerical properties for the study of chronosequences. Geoderma 145:278-287. https://doi.org/10.1016/j.geoderma.2008.03.022
  4. Chan, T.P., and R.S. Govindaraju. 2004. Estimating soil water retention curve from particle-size distribution data based on polydisperse sphere systems. Vadose Zone J. 3 (4):1443-1454. https://doi.org/10.2136/vzj2004.1443
  5. El-Baz and Nayak, 2004. Efficiency of composite sampling for estimating a lognormal distribution. Environ.Ecol.Stat.11:283-294 https://doi.org/10.1023/B:EEST.0000038016.20656.21
  6. Ellis, R.N., P.M. Kroonenberg, B.D. Harch, and K.E. Basford. 2006. Non-linear principal components analysis: an alternative method for finding patterns in environmental data. Environmetrics 17:1-11. https://doi.org/10.1002/env.738
  7. Gifi, A. 1990. Nonlinear Multivariate Analysis. John Wiley, Chichester, UK.
  8. Guber, A.K., W.J. Rawls, E.V. Shein, and Y.A. Pachepsky. 2003. Effect of soil aggregate size distribution on water retention. Soil Sci. 168:223-233.
  9. Guber A.K., Y.A. Pachepsky, M.T.van Genuchten, W.J. Rawl, J. Simunek, D. Jacques, T.J. Nicholson, and R.E. Cady. 2006. Fieldscale water flow simulations using ensembles of pedotransfer functions for soil water retention. Vadose Zone J. 5 (1):234-247. https://doi.org/10.2136/vzj2005.0111
  10. Han, H., D. Gimenez, and A. Lilly. 2008. Textural averages of saturated soil hydraulic conductivity predicted from water retention data. Geoderma. 146:121-128 https://doi.org/10.1016/j.geoderma.2008.05.017
  11. Holden, N.M. 1995. Temporal variation in ped shape in an old pasture soil. Catena 24:1-11. https://doi.org/10.1016/0341-8162(94)00034-C
  12. Horn, R., H. Taubner, M. Wuttke, and T. Baumgarlt. 1994. Soil physical-properties related to soil-structure. Soil till. Res. 30:187-216. https://doi.org/10.1016/0167-1987(94)90005-1
  13. Kass, G.V. 1980. An exploratory technique for investigating large quantities of categorical data. J. Appl. Statist. 229:119-127.
  14. Kay, B.D., and A.R. Dexter. 1990. Influence of aggregate diameter, surface-area and antecedent water-content on the dispersibility of clay Can. J. Soil Sci. 70:655-671.
  15. Kosugi, 1996. Lognormal distribution model for unsaturated soil hydraulic properties. Water Resour. Res. 32:2697-2703. https://doi.org/10.1029/96WR01776
  16. Kosugi, K. 1997. A new model to analyze water retention characteristics of forest soils based on soil pore radius distribution. J. Forest Res. 2:1-8. https://doi.org/10.1007/BF02348255
  17. Kullback, L. 1951. On information and sufficiency. Ann. Math. Stat. 22:79-86. https://doi.org/10.1214/aoms/1177729694
  18. Levine, E.R., D.S. Kimes, and V.G. Sigillito. 1996. Classifying soil structure using neural networks. Ecol. Model. 92:101-108. https://doi.org/10.1016/0304-3800(95)00199-9
  19. Lilly, A., A. Nemes, W.J. Rawls, and Y.A. Pachepsky. 2008. Probabilistic approach to the identification of input variables to estimate hydraulic conductivity. Soil Sci. Soc. Am. J. 72:16-24. https://doi.org/10.2136/sssaj2006.0391
  20. Lin, H.S., K.J. McInnes, L.P. Wilding, and C.T. Hallmark. 1999. Effects of soil morphology on hydraulic properties: I. Quantification of soil morphology. Soil Sci. Soc. Am. J. 63:948-954. https://doi.org/10.2136/sssaj1999.634948x
  21. McKenzie, D.C., T.S. Abbott, and F.R. Higginson. 1991. The effect of irrigated crop production on the properties of a sodic vertisol. Aust. J. Soil Res. 29:443-453. https://doi.org/10.1071/SR9910443
  22. Meulman, J.J., and W.J. Heiser. 1999. SPSS Categories 10.0. SPSS Inc, Chicago, IL.
  23. Nemes, A., W.J. Rawls, and Y.A. Pachepsky. 2006. Use of the nonparametric nearest neighbor approach to estimate soil hydraulic properties. Soil Sci. Soc. Am. J. 70:327-336. https://doi.org/10.2136/sssaj2005.0128
  24. Nikiforoff, C.C. 1941. Morphological classificaton of soil structure. Soil Sci. 52:193-212. https://doi.org/10.1097/00010694-194109000-00003
  25. Olness, A. and D. Archer. 2005. Effect of organic carbon on available water in soil. Soil Sci. 170:90-101. https://doi.org/10.1097/00010694-200502000-00002
  26. Pachepsky, Y.A., and W.J. Rawls. 2003. Soil structure and pedotransfer functions. Eur. J. Soil Sci. 54:443-451. https://doi.org/10.1046/j.1365-2389.2003.00485.x
  27. Pachepsky, Y.A., W.J. Rawls, and H.S. Lin. 2006. Hydropedology and pedotransfer functions. Geoderma 131:308-316. https://doi.org/10.1016/j.geoderma.2005.03.012
  28. Petersen, L.W., P. Moldrup, O.H. Jacobsen, and D.E. Rolston. 1996. Relations between specific surface area and soil physical and chemical properties. Soil Sci. 161:9-21. https://doi.org/10.1097/00010694-199601000-00003
  29. Rawls, W.J., and Y.A. Pachepsky. 2002. Soil consistence and structure as predictors of water retention. Soil Sci. Soc. Am. J. 66:1115-1126. https://doi.org/10.2136/sssaj2002.1115
  30. Rawls, W.J., D. Gimenez, and M. Grossman. 1998. Use of soil texture, bulk density, and slope of wter retention curve to predict saturated hydrualic conductivity. Trans. ASAE 41:983-988. https://doi.org/10.13031/2013.17270
  31. Rawls, W.J., Y.A. Pachepsky, J.C. Ritchie, T.M. Sobecki, and H. Bloodworth. 2003. Effect of soil organic carbon on soil water retention. Geoderma 116:61-76. https://doi.org/10.1016/S0016-7061(03)00094-6
  32. Saxton, K.E., W.J. Rawls, J.S. Romberger, and R.I. Papendick. 1986. Estimating generalized soil-water characteristics from texture. Soil Sci. Soc. Am. J. 50 (4):1031-1036. https://doi.org/10.2136/sssaj1986.03615995005000040039x
  33. Schaap, M.G., F.J. Leij, and M.T. van Genuchten. 2001. ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. J. Hydrol. 251:163-176. https://doi.org/10.1016/S0022-1694(01)00466-8
  34. Schaetzl, R.J., and S. Anderson. 2005. Soil Genesis and Geomorphology. Cambridge University Press, Cambridge, UK.
  35. Scalenghe, R., E. Zanini, and D.R. Nielsen. 2000. Modeling soil development in a post-incisive chronosequence. Soil Sci. 165:455-462. https://doi.org/10.1097/00010694-200006000-00001
  36. Tamboli, P.M., W.E. Larson, and M. Amemiya. 1964. Influence of aggregate size on soil moisture retention. Iowa Acad. Sci. 71:103-108.
  37. Van Genuchten, 1980. A closed form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44:892-989. https://doi.org/10.2136/sssaj1980.03615995004400050002x
  38. Wittmuss, H.D., and A.P. Mazurak. 1958. Physical and chemical properties of aggregates in a Brunizem soil. Soil Sci. Soc. Am. Proc. 22:1-5. https://doi.org/10.2136/sssaj1958.03615995002200010001x
  39. Wosten, J.H.M., C. Schuren, J. Bouma, and A. Stein. 1990. Functional sensitivity analysis of 4 methods to generate soil hydraulic functions. Soil Sci. Soc. Am. J. 54:832-836. https://doi.org/10.2136/sssaj1990.03615995005400030036x
  40. Wosten, J.H.M., A. Lilly, A. Nemes, and C. Le Bas. 1999. Development and use of a database of hydraulic properties of European soils. Geoderma. 90:169-185. https://doi.org/10.1016/S0016-7061(98)00132-3
  41. Yoon S.W. 2009. A measure of soil structure derived from water retention properties: A kullback-Leibler distance approach. Ph.D. Dissertation. Rutgers, The State University of New Jersey, New Brunswick (UMI No. 3359187).